A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

A Surv e y of Air -to-Ground Propagation Channel Modeling for Unmanned Aerial V ehicles W ahab Khaw aja ∗ , Ismail Guvenc ∗ , David W . Matolak † , Uwe-Carsten Fiebig ‡ , Nicolas Schneckenber ger ‡ ∗ Department of Electrical and Computer Engineering, North Carolina State Univ ersity , Raleigh, NC † Department of Electrical Engineering, Univ ersity of South Carolina, Columbia, SC ‡ German Aerospace Center (DLR) Institute of Communications and Navigation, W essling, Germany Email: { wkhawaj, iguv enc } @ncsu.edu, { matolak } @cec.sc.edu, { uwe.ﬁebig, nicolas.schneckenb urger } @dlr .de Abstract —In recent y ears, there has been a dramatic incr ease in the use of unmanned aerial vehicles (U A Vs), particularly f or small U A Vs, due to their affordable prices, ease of availability , and ease of operability . Existing and future applications of U A Vs include remote surv eillance and monitoring, relief operations, package delivery , and communication backhaul infrastructure. Additionally , U A Vs are en visioned as an important component of 5G wireless technology and bey ond. The unique applica- tion scenarios for U A Vs necessitate accurate air-to-gr ound (A G) propagation channel models for designing and evaluating U A V communication links for control/non-payload as well as payload data transmissions. These A G propagation models have not been in vestigated in detail when compared to terrestrial propagation models. In this paper , a comprehensive survey is provided on av ailable A G channel measur ement campaigns, large and small scale fading channel models, their limitations, and future r esearch directions for U A V communication scenarios. Index T erms —Air -to-ground (A G), channel measurement, channel modeling, drone, large and small scale fading, sounding, unmanned aerial vehicle (U A V). I . I N T RO D U C T I O N Use of commercial unmanned aerial vehicles (UA Vs) has recently seen exceptional growth that is forecast to continue in the near future. The beneﬁts of easy operability , multiple ﬂight controls, high maneuverability , and increasing payload weight of currently a vailable U A Vs hav e led to their intro- duction into many real time civilian applications including remote surveillance, ﬁlming, disaster relief, goods transport, and communication relaying, not to mention recreation. Ac- cording to statistics provided by the market research company T ractica, the shipment of commercial UA Vs units is expected to reach 2 . 7 million in 2025 with the services offered rising to $ 8 . 7 billion in the next decade [1]. U A Vs are also termed unmanned aerial systems (U AS), and commonly kno wn by the term “drones. ” These aircraft can vary in size from small toys that ﬁt in the palm of a human hand (where the “unmanned” designation is unnecessary) to large military aircraft such as the General Atomics MQ-9 Reaper (commonly termed Predator) [2], with a wingspan over 15 meters. The small, battery powered toys generally can ﬂy for up to 15 minutes, whereas the larger U A Vs are designed This work has been supported in part by NSF under the grant CNS- 1453678 and by N ASA under the Federal A ward ID number NNX17AJ94A. for long-endurance ( 30 hours), high-altitude operations (higher than 15 km). In this paper our focus is on the smaller U A Vs. V arious organizations have developed classiﬁcations for U A Vs ac- cording to size, with designations large, medium, and small being typical. In the US, the Federal A viation Administration (F AA) has issued rules for small U A Vs weighing less than 55 pounds ( 25 kg) [3]. Highlights of these rules include the requirement for a visual line-of-sight (LOS) from pilot to aircraft, ﬂight under daylight or during twilight (within 30 minutes of ofﬁcial sunrise/sunset) with appropriate lighting for collision av oidance, a maximum ﬂight ceiling of 400 feet ( 122 m) above the ground (higher if the U A V is within 122 m of a construction site), and a maximum speed of 100 mph ( 87 knots, or 161 km/h). Restrictions also apply regarding proximity to airports, and generally , a licensed pilot must operate or supervise U A V operation. One of the promising applications of U A Vs is in supporting broadband wireless cellular communications in hot spot areas during peak demand e vents and in cases of a natural calamity where the existing communication infrastructure is damaged. It is expected that future 5G implementation will include U A Vs as autonomous communicating nodes for providing low latency and highly reliable communications, at least in some situations. Qualcomm is testing the operability of UA Vs for current L TE and future 5G cellular applications [4]. In addition, UA Vs can act as mobile wireless access points in different network topologies supporting different protocols of IEEE 802 . 11 . Facebook and Google are also exploring the possibility of using U A Vs for Internet connectivity to remote areas using U A Vs [5]. Air-to-ground (A G) communications can be traced back to 1920 [6], with manually operated radio telegraphs. Lower and medium frequency bands were used in the early 1930 s but did not support simultaneous voice communications in both directions (AG and ground-to-air (GA)). From the early 1940 s , double sideband amplitude modulation (DSB-AM) in the frequenc y band ( 118 MHz - 137 MHz) lying in the very high frequency (VHF) band was adopted for voice communications between pilots and ground controllers. This system supported a maximum of 140 channels until 1979. Multiplexing and multiple access were frequency di vision with manual channel assignment by air trafﬁc control. In more dense air trafﬁc spaces, to enable larger numbers of simultaneous transmissions, 25 kHz DSB-AM channels were subdivided into three channels of width 8.33 kHz. The civilian aeronautical AG communications continues to use the reliable analog DSB-AM system today , although since 1990 some small segments of the VHF band in some geographic locations are being upgraded to a digital VHF data link that can in principle support 2280 channels [7], [8]. This system employs time-division as well as frequency-di vision, with single-carrier phase-shift keying modulation. Military AG communications uses different frequency bands (ultra-high frequency) and modulation schemes for short and long ranges [9]. Due to very low data rates, the civil aviation systems cannot support mod- ern AG communication requirements. In 2007 , use of portion of the L-band was suggested for new civil aviation systems, and two such systems known as L-band Digital Aeronautical Communications Systems, or LD A CS, were developed [8]. Due to compatibility with numerous existing systems that operate in the L-band, the LD ACS system is still being reﬁned. LD ACS is currently being standardized by the International Civil A viation Organization (ICA O). There are numerous studies a vailable in the literature on the characteristics of aeronautical channels [7], [10]–[13]. Aeronautical communications can be broadly classiﬁed into communications between the pilot or cre w with the ground controller and wireless data communication for passengers. Both of these types of communication are dependent on the ﬂight route characteristics. In [10] the propagation channel is divided into three main phases of ﬂight, termed as parking and taxiing, en-route, and take off and landing. Each phase of ﬂight was described by dif ferent channel characteristics (type of fading, Doppler spread, and delay), but this relativ ely early paper was not comprehensive nor fully supported by measurements. There are also long distance A G propagation channel studies av ailable for satellites and high altitude platforms (HAPs). The A G propagation channel in these studies can be con- sidered as a U A V communication channel, but due to long distances from the earth surface, normally greater than 17 km, modeling of these links may also need to take into account upper atmospheric ef fects. Depending on frequency and U A V altitude, the y may also be much more susceptible to lo wer tropospheric effects such as fading from hydrometeors [14]. For most of these longer distance platforms, a LOS component is required because of po wer limitations, hence the A G channel amplitude fading is typically modeled as Ricean [15]. As the deployment of U A Vs as communication nodes in the near future is expected to be at much lo wer altitudes compared to that of HAPs and satellites, in this survey we focus only on lower altitude U A V AG propagation channels. In order to fulﬁll the ev er increasing demands of high rate data transfer in the future using U A Vs in different environ- ments, robust and accurate AG propagation channel models are required. The available A G propagation channel models used for higher altitude aeronautical communications generally cannot be employed directly for low-altitude UA V communi- cations. Small U A Vs may also possess distinct structural and ﬂight characteristics such as dif ferent airframe shado wing fea- tures due to unique body shapes and materials, and potentially sharper pitch, roll, and yaw rates of change during ﬂight. The A G channel for U A Vs has not been studied as extensi vely as the terrestrial channel. The av ailable UA V based A G wireless propagation channel research can be largely categorized into two major portions. The ﬁrst one is payload communications, where the payload can be narrow-band or wide-band and is mostly application dependent. The second one is control and non-payload com- munications (CNPC) for telemetric control of U A Vs. Most payload UA V communications employs the unlicensed bands e.g., 900 MHz, 2 . 4 GHz, and 5 . 8 GHz; this is not preferred by the aviation community as these bands can be congested and may be easily jammed. In the USA, CNPC is potentially planned for a portion of L-band ( 0 . 9 GHz - 1 . 2 GHz) and C-band ( 5 . 03 GHz - 5 . 091 GHz), although as is common in spectrum allocation, use of these bands is still being negotiated [16], [17]. Channel measurements and modeling for U A Vs are (other than bandwidth and carrier frequency) largely independent of whether signaling is for payload or CNPC. In this study , we will discuss recent channel measurement campaigns and modeling efforts to characterize the A G chan- nel for U A Vs. W e also describe future research challenges and possible enhancements. T o the best of our knowledge, there is to date no comprehensiv e survey on A G propagation channel models for U A V wireless communications. The rest of the paper is organized as follo ws. Section II explains the UA V A G propagation channel characteristics. The A G channel measurements and associated features are described in Section III, and Section IV discusses AG propa- gation channel models, including models based on ray tracing simulations. Future challenges and research directions are provided in Section V , and concluding remarks follow in Section VI. All acronyms and variables used throughout the paper are giv en in T able I and T able II, respectiv ely . I I . UA V AG P R O P AG AT IO N C H A N N E L C H A R A C T E R I S T I C S In this section, salient characteristics of U A V A G propaga- tion channels are described. A common A G propagation sce- nario is shown in Fig. 1 in the presence of terrestrial obstacles which are also commonly referred as scatter ers . In the ﬁgure, h G , h S , h U represents the height of the ground station (GS), scatterers, and UA V abov e the ground, respectiv ely , d is the slant range between the U A V antennas and the GS, and θ is the elev ation angle between GS and U A V antennas. (W e note that airborne scatterers may be present as well, but for this paper , for the AG link, we neglect this secondary condition.) A. Comparison of UA V AG Pr opagation with T err estrial The A G channel exhibits distinctly dif ferent characteristics from those of other well studied terrestrial communication channels, e.g., the urban channel. On the one hand, there is the inherent advantage over terrestrial communications in terms of a higher likelihood of LOS propagation.This reduces transmit Scat te rer s GS Gr ou nd le vel h G h S UA V θ d TX/RX TX /R X h U Gr ou nd reflec te d path z x y Scat te rer s Fig. 1: A typical air-to-ground propagation scenario with a U A V . power requirements and can translate to higher link reliability as well. In cases where only non-LOS (NLOS) paths e xist, when the elev ation angle to the U A V is large enough, the A G channel may incur smaller diffraction and shadowing losses than near ground terrestrial links. On the other hand, the A G channel can exhibit signiﬁcantly higher rates of change than typical terrestrial communication channels because of U A V velocities. When the channel is mod- eled statistically , this can mean that the channel’ s statistics are approximately constant (the channel is wide-sense stationary) for only a small spatial extent. This is often loosely termed ”non-stationarity . ” If the UA V is not in the direct vicinity of scattering objects or the GS, the characteristics of the channel could instead actually change very slo wly , especially for hov- ering UA Vs. In such a case, adverse propagation conditions, e.g., deep f ades of the recei ved signal, may las t sev eral seconds or e ven minutes, hence common communication techniques of interleaving or av eraging would not be effecti ve. In many cases, when UA V altitudes are well abov e scattering objects, the A G channel’ s ”non-stationarity” will be attrib utable to the direct surroundings of the GS, e.g., the close by buildings or the ground surface composition around the GS. Additionally , A G communications with U A Vs face many other challenges, due to arbitrary mobility patterns and div erse types of communication applications [18]–[21]. As an aerial node, some of the U A V speciﬁcs that need to be taken into ac- count include airframe shadowing, mechanical and electronic noise from U A V electronics and motors, and ﬁnally antenna characteristics, including size, orientation, polarization, and ar- ray operation (e.g., beam steering) for multiple-input-multiple- output (MIMO) systems. For U A Vs in motion, the effect of Doppler shifts and spread must also be considered for speciﬁc communication applications [22], [23]. For a given setting, an optimum U A V height may need to be considered, e.g., for maintaining LOS in that en vironment [24]. B. F requency Bands for UA V AG Propa gation As with all communication channels, a fundamental consid- eration is the frequency band, since propagation characteristics can vary signiﬁcantly with frequency . For the L and C- bands envisioned for CNPC, and for the currently popular unlicensed bands for payload communications, tropospheric attenuations from atmospheric gases and hydrometeors are mostly negligible. This will not be true for operation at higher frequency bands e.g., at Ku, Ka, and other so-called millimeter wa ve (mmW av e) bands, which may be as high as 100 GHz. These higher frequenc y bands can hence suf fer both lar ger free-space path loss (PL) as well as tropospheric attenuations. Because of this, these frequency bands will generally be used for short-range A G links. In contrast to the attenuation characteristics compared with lower frequency bands, mmW ave bands offer a large amount of bandwidth, which is their primary appeal for 5G cellular systems. Large bandwidths can be more robust to the larger values of Doppler shift and Doppler spread encountered with U A Vs moving at high velocity . C. Speciﬁcs of UA V AG Pr opagation Channel In an A G propagation channel using U A Vs, the multipath components (MPCs) appear due to reﬂections from the earth surface, from terrestrial objects (ground scatterers), and some- times from the airframe of the UA V itself. The characteristics of the channel will be dependent on the material, shape, and size of the scattering objects. The strongest MPC apart from the LOS component in an AG propagation scenario is often the single reﬂection from the earth surface. This giv es rise to the well known two ray model. For high enough frequencies, the scatterers on the ground and around the U A V can be modeled as points scatterers on the surface of two respectiv e cylinders or spheres [25], [26] or el- lipsoids, and these can be bounded (truncated) by intersection of the elliptical planes on the ground [27], [28]. These topolo- gies can help in deriving geometrical characteristics of the A G Acronym T ext Acronym T ext AA Air-to-air A G Air-to-ground A WGN Additiv e white Gaussian noise BER Bit error rate BPSK Binary phase shift ke ying BW Bandwidth CDF Cumulativ e distribution function CFO Carrier frequency offset CIR Channel impulse response CNPC Control and non-payload communications CSI Channel state information CTF Channel transfer function CW Continuous wa ve DPP Doppler power proﬁle DS Doppler spread DSB-AM Double sideband amplitude modulation DS-SS Direct sequence spread spectrum F AA Federal aviation administration FMBC Filter bank multicarrier FMCW Frequency modulated continuous wave GA Ground-to-air GMSK Gaussian Minimum Shift K eying GPS Global positioning system GS Ground station GSM Global system for mobile communication HAP High altitude platform ICI Inter-carrier interference IS-GBSCM Irregular shaped geometric based stochastic channel model LAP Lower altitude platform LD A CS L-band digital aeronautical communications LOS Line-of-sight L TE Long term evolution MIMO Multiple-input-multiple-output MISO Multiple-input-single-output Mod. Sig. Modulated signal MPC Multipath component MSK Minimum shift keying NGSCM Non-geometric channel model NLOS Non-line-of-sight OFDM Orthogonal frequency-di vision multiplexing OLOS Obstructed line-of-sight P APR Peak to av erage power ratio PDP Power delay proﬁle PG Path gain PL Path loss PLE Path loss exponent PRN Pseudo-random number PSD Power spectral density RF Radio frequency RMS-DS Root mean square-delay spread RS-GBSCM Regular shaped geometric based stochastic channel model RSS Receiv ed signal strength RSSI Receiv ed signal strength indicator R TT Round trip time RX Receiver SDMA Space-division multiple access SIMO Single-input-multiple-output SISO Single-input-single-output SNR Signal-to-noise-ratio TDL T ap-delay-line TDMA T ime division multiple access TO A T ime-of-arriv al TX T ransmitter U AS Unmanned aerial systems U A V Unmanned aerial vehicle UMTS Univ ersal Mobile T elecommunications Service UWB Ultra-wideband VHF V ery high frequency WSS W ide sense stationary T ABLE I: Acronyms used in this paper . propagation scenario. The distrib ution of scattering objects, on land or water , can be modeled stochastically , and this concept can be used to create so-called geometrically-based stochastic channel models (GBSCMs). For aircraft moving through an area above such a distribution, this gives rise to intermittent MPCs [29], as also seen in vehicle-to-v ehicle channels. In order to describe the statistical characteristics of a fading channel, typically ﬁrst and second order fading statistics are used. The majority of the A G literature discusses ﬁrst order fading statistics. The second order statistics of en velope lev el crossing rate and av erage fade duration are discussed in [25], [30], but many authors address other second order properties, primarily correlation functions in the time or frequency do- mains. In case of propagation over water the PL is similar to that of free space [31], with a strong surface reﬂection. The other MPCs from the water surface are weaker , and of approximately equal power and time-of-arriv al (T oA), whereas MPCs from obstacles on the water surface, e.g., large ships, can be stronger . D. Antenna Conﬁgurations for U A V A G Pr opagation The antenna is one of the critical components for AG communications due to limited space, and limitations of the aerodynamic structure [32], [33]. Factors that affect AG link performance are the number, type and orientation of the an- tennas used, as well as the U A V shape and material properties. The majority of A G channel measurements employ stand alone (single) antennas, whereas in [34], an antenna array is used. There are some SIMO and MIMO antenna conﬁgurations av ailable in the literature for A G propagation measurements [35], [36]. Omni-directional antennas are most popular for vehicular communications due to their superior performance during motion, whereas directional antennas (having better range via directional gain) can perform poorly during motion due to mis-alignment losses. W ith high maneuverability of U A Vs during ﬂight, omni-directional antennas are generally better suited than directional antennas. A potential major dra w- back of any antenna on-board U A Vs is the shadowing from the body of the UA V . Similarly , orientation of antennas on-board U A Vs can affect the communication performance [37], [38]. The use of multiple antennas to enable div ersity can yield spatial diversity gains ev en in sparse multipath en- Acronym T ext a i Amplitude of i th MPC c Speed of light d Link distance between TX and RX d 0 Reference distance between TX and RX f Frequency instance f c Carrier frequency f i d Doppler frequency shift of i th MPC h G Ground station height h S Height of scatterer h U U A V altitude above ground K -factor Ricean K -factor M T otal number of MPCs p i ( t ) MPCs persistence coef ﬁcient P R Receiv ed power P T T ransmit power P L 0 Reference path loss t Time instance v V elocity of U A V v max Maximum speed Θ Aggregated phase angles γ Path loss exponent τ i Delay of i th MPC λ W avelength of the radio wave φ i Phase of the i th MPC X Shadowing random variable θ Elev ation angle σ Standard deviation of shadow fading ∆ ψ Phase difference between the LOS and ground reﬂected MPC ς Ratio of built up area to total area ξ Mean number of buildings per unit area Ω Height distribution of buildings α Slope of linear least square regression ﬁt β Y -intercept point for the linear least square regression ﬁt T ABLE II: V ariables used in this paper . vironments [39], [40]. Similarly , multiple antennas can be used for spatial selecti vity such as beam forming/steering. Howe ver , due to limited space on-board U A Vs, space div ersity using multiple antennas is dif ﬁcult to achiev e, especially for lower carrier frequencies. Beamforming using antenna arrays operating at mmW av e frequencies, for example, can be used to overcome f ading and impro ve co verage, but ar - ray processing will require high computational resources on- board. The employment of MIMO systems for enhancing the channel capacity of the A G propagation channel has been suggested in [41], [42]. By changing the diameter of a circular antenna array and the U A V ﬂying altitude, dif ferent values of MIMO channel capacity were obtained [41]. Whereas in [42], optimizing the distance between the antenna elements using linear adapti ve antenna arrays was proposed to increase MIMO channel capacity . E. Doppler Effects Due to U A V motion, there are Doppler frequency shifts that depend on the velocity of the U A V and the geometry . Higher Doppler frequency presents a problem if the different signal paths are associated with lar gely dif ferent Doppler frequencies, yielding large Doppler spread. This can happen if the aircraft is relativ ely close to the GS. If the aircraft is further away from the GS, and at sufﬁcient altitude, the paths should all hav e a very similar Doppler frequency as the objects in the close surroundings of the GS causing MPCs are seen all under similar angles from the aircraft. The effect of a large Doppler frequenc y that is constant for all MPCs should be well mitigated by frequency synchronization. Doppler shifts can introduce carrier frequency offset (CFO) and inter carrier interference, especially for orthogonal frequency division mul- tiplexing (OFDM) implementations. There are several studies that consider modeling of Doppler spread [10], [22], [23], [30], [43]–[46]. Some channel access algorithms e.g., multi carrier code di vision multiple access, hav e been shown to be robust against Doppler spread in A G propagation [47]. I I I . AG C H A N N E L M E A S U R E M E N T S : C O N FI G U R A T I O N S , C H A L L E N G E S , S C E N A R I O S , W A V E F O R M S Sev eral A G channel measurement campaigns using piloted aircraft and UA Vs hav e been recently reported in the literature. These measurements were conducted in dif ferent en vironments and with different measurement parameters. In this section, we provide a brief classiﬁcation of these measurements based on environmental scenario, sounding signal, carrier frequency , bandwidth, and antenna speciﬁcations and placement. As av ailable, we also provide U A V type and speed, heights of U A V and GS from terrain surface, link distance between transmitter (TX) and receiv er (RX), elev ation angle, and the channel statistics provided by the cited authors. These channel measurement parameters are giv en in T able III. In the reported A G propagation measurements, either TX or RX on UA V/GS is stationary . Measurements with both TX and RX moving for A G propagation are rare. A notable contribution of wide-band A G propagation measurements is av ailable in the form of multiple campaigns conducted in the L and C bands using single-input-multiple-output (SIMO) antenna conﬁguration for different terrain types and o ver water/sea [29], [31], [35], [49]–[55]. The rest of the cited channel measurements are conducted in different frequenc y bands ranging from narro w-band to ultra-wideband (UWB) with various types of sounding signals. A. Channel Measurement Conﬁgurations These channel measurements used different types and con- ﬁgurations of antennas. The most commonly used antenna type is omni-directional and the most commonly used conﬁguration is single-input-single-output (SISO). The positioning of an antenna on the U A V is important to av oid both shadowing from the airframe and disruption of the aircraft’ s aerodynamics. In the majority of measurements the antennas were mounted on the bottom of the aircraft’ s fuselage or wings. The orientation of antennas on UA V and ground can also af fect the signal characteristics [37], [38], [60], [61]. This characteristic is most important during banking turns, and when the aircraft pitch angle deviates from horizontal. The elev ation angle between TX and RX antennas is dependent on the height of UA V and GS and often continuously varies during the ﬂight. Ref. Scenario Sound. Sig. Freq. (GHz) BW (MHz) Antenna and mounting P T P T P T (dBm) U A V , v max v max v max (m/s) h U h U h U , h G h G h G , d (m) θ θ θ (deg.) Channel statistics [30] Urban CW 2 . 0125 1 Monopole on U A V for TX, 4 on GS for RX 27 Air balloon, 8 170 , 1 . 5 , 6000 1 - 6 P R , Auto-correlation of direct and dif fuse components [48] Open ﬁeld, suburban PRN 3 . 1 - 5 . 3 2200 1 Dipole on U A V for TX and 1 on GS for RX 14 . 5 Quad-copter , 20 16 , 1 . 5 , 16 . 5 - PL, PDP , RMS-DS, TO A of MPCs, PSD of sub-bands [29], [31], [35], [49]– [55] Urban, suburban, hilly , desert, fresh water , harbor , sea DS-SS 0 . 968 , 5 . 06 5 , 50 1 directional antenna on GS for TX, 4 monopoles on U A V for RX 40 Fixed wing, 101 520 − 1952 , 20 , 1000 − 54390 1 . 5 - 48 PL, PDP , RMS-DS, K-factor , tap probability and statistics (power , delay , duration) in TDL model [56] rural, suburban OFDM 0 . 97 10 1 monopole antenna on GS for TX, 1 monopole on aircraft for RX 37 Fixed wing, 235 11000 , 23 , 350000 0 - 45 PL, PDP , DPP [57] rural, suburban, urban, forest FMCW 5 . 06 20 1 monopole on UA V for TX, 1 patch antenna on GS for RX 30 Fixed wing, 50 -, 0 , 25000 - CIR, PG, RSS [58] Urban MSK 2 . 3 6 1 Whip antenna on UA V as transceiv er , 1 patch antennas as transceiv er on GS 33 Fixed wing, 50 800 , 0 . 15 , 11000 4 . 15 - 86 RSS [59] Urban, suburban, rural GSM, UMTS 0 . 9 , 1 , 9 - 2 , 2 - T ransceiver on balloon and GS 41 . 76 Captiv e balloon 450 , -, - - RSSI, handov er analysis [36] Urban, hilly , ocean OFDM 2 . 4 4 . 375 4 whip antennas on A V for TX, 4 patch antennas on GS for RX - Fixed wing, 120 3500 , -, 50000 - Eigen values, beam-forming gain [39] Rural PRN, BPSK 0 . 915 10 2 helical antennas on A V for TX, 8 at GS for RX 44 . 15 Fixed wing, 36 200 , -, 870 13 - 80 CIR, P R , RMS-DS, spatial div ersity [37] - OFDM 5 . 28 - 4 omni-directional on U A V for TX, 2 on GS for RX 18 Fixed wing, 17 . 88 45 . 72 , 4 . 26 , - - P R , RSSI [38] Urban, open ﬁeld OFDM 5 . 24 - 2 omni-directional on U A V for TX, 2 on GS for RX 20 Quad- copter , 16 120 , 2 , 502 . 5 - RSSI [60] Open ﬁeld OFDM 5 . 24 - 3 omni-directional on UA V for TX, 3 on GS for RX 20 Quad- copter , 16 110 , 3 , 366 . 87 10 - 85 RSS [61] - IEEE 802 . 15 . 4 2 . 4 - On board inv erted F transceiv er antenna on U A V and GS 0 Hexacopter , 16 20 , 1 . 4 , 120 - RSSI [20] Suburban W iﬁ, 3 G/ 4 G - - T ransceiver on UA V and GS - Hexacopter , 8 100 , -, - - P R , R TT of packets [62] Forest (anechoic chamber) - 8 - 18 - Spiral antennas on TX and RX - - 2 . 3 , 0 . 6 , 2 . 85 26 - 45 P R [63] Open area Mod. sig. 5 . 8 - 2 Monopole, 1 horn on U A V for TX, 2 on GS for RX - Fixed wing,- 150 , 0 , 500 - P R [64] Open area/foliage 802 . 11 b/g 5 . 8 - 1 omni-directional on GS for TX, 4 on U A V for RX - Fixed wing, 20 75 , . 2 , - - Div ersity performance [65] Urban/ suburban, open ﬁeld, foliage CW 2 . 00106 , 2 . 00086 - 2 monopoles on U A V for TX, 2 on GS for RX 27 Gondala airship, 8 . 3 50 and above, 1 . 5 , 2700 1 P R [66] Urban, rural, open ﬁeld 0 . 915 - 1 omni-directional antenna on U A V for TX, 1 on GS for RX - Quad- copter ,- -, 13 . 9 , 500 - RSSI, PL [67] Sea PRN 5 . 7 - Omni-directional on A V for TX, 2 directional antennas at GS for RX 40 Fix ed wing A V ,- 1830 , 2 . 1 , 7 . 65 , 95000 - PL [34] Urban CW 2 . 05 - 1 monopole on A V for TX, 4 on GS for RX - Aerial platform,- 975 , -, - 7 . 5 - 30 PDP , RMS-DS, MPCs count, K -factor , PL [47] Near airport CW 5 . 75 - Directional antenna on GS for TX and omni-directional on A V for RX 33 Fix ed wing A V ,- 914 , 20 , 85000 80 P R , Fading depth, K -factor , PL [68] Urban, hilly Chirp 5 . 12 20 1 monopole antenna on GS for TX and 1 omni-directional on A V for RX 40 Fix ed wing A V , 293 11000 , 18 , 142000 ( − 16) − 5 PDP T ABLE III: Important empirical A G channel measurement studies in the literature. In the majority of the communication applications en vi- sioned for U A Vs, the aerial node is expected to be stationary (or mostly so) in space for a giv en time. As noted, for communications with a mobile U A V , the velocity will affect the channel statistics. For U A Vs operating at higher velocities, the coherence time of the channel decreases, and this translates into a larger Doppler spread. For connections to multiple U A Vs, where hand-overs are required, this means that the num- ber of handov ers will also generally increase with velocity , and this will require additional processing. Additionally , higher velocities will result in increased air friction and mechanical turbulence that generally result in increased noise lev els. Many of the AG channel measurements in the literature have been conducted with ﬁxed wing aircraft with maximum speeds varying from 17 m/s to 293 m/s. The speed of rotorcraft and air balloons is much less than that of ﬁxed wing aircraft, and ranges from 8 m/s to 20 m/s. The height of the U A V above ground is an important chan- nel parameter and will also affect the channel characteristics. For example, increasing the height of the U A V usually results in reduced effect of MPCs [69] from surrounding scatterers. Another beneﬁt of higher U A V altitude is larger coverage area on the ground. Similarly , the height of GS will also affect the channel characteristics. For a giv en en vironmental scenario, there may be an optimal height of the GS [48],e g., this might be a balancing of attenuation and multipath div ersity . Example propagation measurements using rotorcraft and air balloons during ﬂight and hovering are av ailable in [30], [38], [48], [61]. These A G propagation measurements were obtained at different U A V heights ranging from 16 m to 11 km, and link distances 16 . 5 m to 142 km. The UA V latitude, longitude, yaw , pitch, and roll readings are typically obtained from GPS RXs and often stored on-board. Apart from con ventional A G channel sounding, there are some indirect UA V A G channel measurements av ailable from use of radios employing different versions of protocols of the IEEE 802.11 standards [37], [38], [60], [61]. The IEEE 802 . 11 supported devices offer a very ﬂexible platform and may provide insight for U A V deployments in different topologies and applications, e.g., U A V swarms. Y et because of the speciﬁc features of 802 . 11 , the resulting measurements are applicable to particular protocol setup and radio conﬁguration, and rarely provide detailed propagation channel characteristics. Air-to-air (AA) communications with U A Vs has not been studied extensi vely in the literature [70]. The AA communi- cations is particularly important for scenarios where multiple drones communicate among a swarm. This swarm then usually communicates with one or more GS via a back-haul link from one or sev eral of the UA Vs. The AA communications is similar to free space with a strong LOS and often a weak ground reﬂection, but this is dependent on the ﬂight altitude and environment. The communication channel is mostly non- dispersiv e for higher altitudes b ut can be rapidly time-varying, dependent on the relativ e velocities of the U A Vs and the scattering en vironment [71]. B. Challenges in A G Channel Measur ements There are many challenges in A G channel measurement campaigns as compared to terrestrial measurements. The biggest challenges are the payload limitation of the U A Vs, and the operating range and height of U A Vs, which in the USA is set by the F AA [72]. Larger U A Vs also incur larger test costs. Due to restrictions on the height of U A Vs above ground, UA Vs at lower altitudes hav e lo wer LOS probability and are hence more susceptible to shado wing, especially in suburban and urban areas. Due to limitations on payload, higher transmit power measurements on-board the U A Vs are difﬁcult to achiev e, and similarly , complex RX processing on- board U A Vs can consume a prohibitiv e amount of power . Other challenges include varying conditions of the terrain during ﬂight, meteorological conditions (winds and rain), antenna positioning on the U A V , precise location measurement of U A Vs in space over time, div erse telemetry control for different types of UA Vs having speciﬁc latencies, bandwidth and reliability issues, and limited ﬂight time for most small U A Vs due to limited battery life [18]–[20]. Due to the motion of UA Vs in three dimensional space, it is challenging to precisely measure the distance between the U A V and the GS. Momentary wind gusts that cause sudden shifts in U A V position can make it difﬁcult to accurately track the UA V path. The most common technique of measuring the instantaneous distance is by using global positioning system (GPS) traces on both the U A V and GS, but of course GPS devices ha ve accu- racy limitations and navigation signals may also be susceptible to interference in different ﬂying zones. C. AG Pr opagation Scenarios A typical type of terrestrial channel sounding equipment, a vector network analyzer , cannot be used for UA V based A G channel sounding due to payload constraints, physical synchronization link requirements, and U A V mobility [73]. Therefore, channel sounding for both narrow-band and wide- band channels using impulse, correlative, or chirp sounding techniques are employed, where the RX is typically on the ground due to payload and processing constraints. Proper selection of channel measurement parameters in a giv en en vironment is critical for obtaining accurate channel statistics for a given application. The A G propagation en- vironment is generally classifed on the basis of the terrain type, namely ﬂat, hilly , mountainous, and ov er water . A particular terrain can have a giv en cover e.g. grass, forest, or buildings. The most widely accepted terrain cover classi- ﬁcation is provided by the International T elecommunication Union (ITU) [74]. In this survey we classify the cited mea- surement scenarios as open (ﬂat), hilly/mountainous, and ov er water . Each scenario can be subdivided on the basis of the terrain cov er as shown in Fig. 2. For any environment, different types of radio controlled U A Vs can be used. Balloons or dirigibles are simple to operate but do not ha ve rob ust mov ement characteristics. The non- balloon U A Vs can be broadly classiﬁed as ﬁxed wing and rotorcraft. The ﬁxed wing U A Vs can glide and attain higher Scenario Characteristics of scenario Important factors Urban/suburban Ratio of land area vs ratio of open to built-up area, distribution of building sizes and heights, distribution of ground terminals (vehicles, pedestrians), distribution and characteristics of ve getation, water bodies, etc. Material of buildings and rooftops Rural/open ﬁeld T ype and density of vegetation, distribution and sizes of the sparse buildings Surface roughness, soil type, and moisture content Hilly/mountainous T errain heights and slopes, distribution and type of vegetation, distribution and sizes of b uildings Ground slope, ground roughness Forest Density and types of foliage, and height distrib utions Leav es and branches distribution Over water W ater type (sea or fresh), distributions and sizes of water surface objects (boats, platforms, etc.), distributions of littoral objects (buildings, water tanks, etc.), and water surface v ariation (e.g., sea state) Modiﬁed reﬂection coefﬁcient as compared to ground, ducting effect in case of over sea T ABLE IV: U A V A G propagation characteristics for ﬁ ve different scenarios. A G p r o p a g a t i o n s c e n a r i o s A e r i a l v e h i c l e s M e a s u r e m e n t e n v i r o n m e n t H i l l y U r b a n S u b u r b a n R u r a l M a n n e d a i r c r a f t F i x e d w i n g R o t o r c r a f t A i r b a l l o o n O p e n ( f l a t ) O v e r w a t e r B u i l d i n g s , m a n m a d e c o n s t r u c t i o n s a n d v e g e t a t i o n U n m a n n e d a i r c r a f t M o u n t a i n o u s F o r e s t / v e g e t a t i o n U r b a n S u b u r b a n B u i l d i n g s , m a n m a d e c o n s t r u c t i o n s a n d v e g e t a t i o n R u r a l Fig. 2: Measurement scenarios for U A V A G propagation channel. air speeds and generally travel farther than the rotorcraft, but rotorcraft are more agile, e.g., most can move straight vertically . Rotorcraft also have the ability to hov er , which is not possible for nearly all ﬁxed wing UA Vs. The U A V AG propagation scenarios in dif ferent en vironments with particular characteristics are described in T able IV. In the rest of this subsection, we revie w the different A G measurement scenarios depicted in Fig. 2. 1) Open Space: A major part of the literature on AG propagation covers open (ﬂat) terrain. This open terrain can hav e dif ferent terrain co vers that af fect the channel charac- teristics. One of the major terrain co ver types is buildings. The distribution of building sizes, heights, and their areawise densities allows sub-classiﬁcation into urban, suburban and rural areas as depicted in Fig 2. In case of urban and suburban areas, there is a higher concentration of man made structures in a gi ven space, e.g., buildings, roads, bridges, large signs, etc. The distribution (and composition) of these complex scatterer structures can strongly inﬂuence the channel characteristics. In rural areas, typically buildings are sparse, and of lo wer height than in urban settings, although large warehouses and other structures could yield strong MPCs. 2) Hilly/Mountainous: The hilly/mountainous terrain is characterized by uneven ground heights; equiv alently , a large standard deviation of terrain height. The propagation PL in hilly and mountainous areas will mostly follow the two ray model with adjustments due to surface roughness, and poten- tially reﬂections from smooth sections of mountain slopes or an occasional large building. The PL over or beyond terrain obstructions can employ established models for diffraction, e.g., [75] but with ﬁrst Fresnel zone clearance between TX and RX, PL is close to free space [51], [52]. Channel dispersion, 0 0.2 0.4 0.6 0.8 1 (a) Time (s) -1 -0.5 0 0.5 1 Amplitude -2 -1 0 1 2 (b) Time (us) -1 -0.5 0 0.5 1 Amplitude 10 20 30 40 50 (c) Time (ms) -1 -0.5 0 0.5 1 Amplitude 0 5 10 15 (d) OFDM symbols 0 10 20 30 40 50 60 Subcarrier indices Pilot Gu a rd band Data Fig. 3: Sounding signals (a) Chirp signal, (b) Short duration Gaussian pulse sounding signal at center frequency of 1 MHz and fractional bandwidth of 60% , (c) PRN sequence of polynomial degree 10 shown half of the overall period, (d) OFDM sounding signal resource mapping with 64 sub-carriers, 16 symbols and 6 pilots. typically quantiﬁed by the RMS-DS, is generally smaller than in urban/suburban environments [52] but can be large if a strong reﬂection occurs from a large and distant mountain slope. Generally , hilly and mountainous settings present fewer reﬂections than more populated regions because of the absence of large numbers of nearby scatterers. 3) F or est: There are few comprehensive studies cov ering A G propagation in forests, especially with UA Vs, although there are numerous publications for roadside shadowing for satellite channels, e.g., [76]–[78]. In these studies, propagation effects–typically attenuation–from particular volumes of trees, along with temporal fade statistics are analyzed for long range A G communications. Generally for A G propagation with a GS within a forest, the channel characteristics are dominated by the type and density of trees. Small U A Vs within a forest experience different scattering characteristics depending upon height, e.g., the scattering near the tree trunk will be different from that near the tree cro wn [62]. The scattering is also dependent on the type and density of leaves and branches of the trees, and hence for deciduous trees, can vary seasonally . 4) W ater/Sea: The A G propagation channel for ov er water settings is similar to that for open settings, with different surface reﬂectivity and roughness than ground. The PL can be represented using a two ray PL model, with variations attributable to surface roughness (see small-scale fading in the following section). The RMS-DS in this case is generally smaller than in environments with a large number of obstacles (urban, suburban), although if large objects are on or just of f shore, these may produce signiﬁcant reﬂections and lar ge delay spreads if geometry permits. In case of propagation ov er sea, the height of waves in a rough sea can introduce additional scattering and ev en diffraction for very lo w height stations on the sea. An in- teresting propagation phenomenon that can also occur over sea is ducting, where anomalous index of refraction variation with height results in propagation loss less than that of free space [67]. This phenomenon is dependent on frequency and meteorological conditions, and is thus typically addressed statistically [79]. D. AG Channel Sounding W aveforms As noted in [44], [68], common channel sounding signals include short pulses (approximately impulses), direct sequence spread spectrum (DS-SS) signals for correlati ve processing, linearly varying frequency modulation (chirp) signals, and multi-tone signals. Different example sounding signals are shown in Fig. 3 representing a chirp signal, RF Gaussian pulse, pseudo-random number (PRN) sequence, and orthogonal fre- quency division multiplexing (OFDM) sounding signals. These sounding signals have been used in dif ferent measurement campaigns summarized in T able III in different A G channel measurement scenarios giv en in Fig. 2. Short duration pulses are direct approximations of input impulses and MPCs can be directly measured in the time domain (e.g., via a sampling oscilloscope). The primary drawback is generation of sufﬁcient pulse energies to reach long distances, and large peak-to- av erage po wer ratios (P APR). The DS-SS signaling uses pseudo-random (PR) sequences to generate a wideband noise- like signal that is demodulated with a sliding (or sometimes a stepped) correlator; this correlation processing yields an estimate of the channel impulse response (CIR). The DS- SS technique can use binary phase shift keying transmission and with modest ﬁltering this yields a low P APR. Chirp sounding has the advantage of high frequency resolution and the potential to sweep over large frequency ranges; P APR can be the ideal v alue of unity . The chirp technique yields the channel transfer function, from which the CIR is obtained via in verse Fourier transformation. Another popular technique is the use of a multitone signal, with the idea of sampling the channel transfer function. This is in essence an OFDM based channel sounding. One advantage of using OFDM sounding is that known data can be used for sounding, hence allowing some data transmission along with channel sounding [80]. The OFDM signals hav e the advantage of a ﬂat spectrum but of course a sinc ( sin( x ) /x ) delay domain response and a large P APR. Details on these various sounding signals can be found in the literature, e.g., [81]. Different carrier frequencies can be used to sound the A G channel and in principle this is completely arbitrary , but most measurements aim at frequency bands in which U A V use is at least possible. Measurements hav e ranged from 100 MHz to 18 GHz with perhaps most of the measurements carried out in the 5 GHz band ( 5 . 06 GHz - 5 . 8 GHz). Similarly , sounding signal bandwidth varies, from very narrow-band to se veral tens of MHz or more. In [48], UWB channel sounding with a bandwidth of 2 . 2 GHz was used, yielding sub-nanosecond time resolution. I V . UA V AG P RO PAG A T I O N M E A S U R E M E N T / S I M U L A T I O N R E S U L T S I N T H E L I T E R AT U R E Sev eral types of channel statistics are useful for characteriz- ing the channel for dif ferent applications. For A G propagation, the channel statistics are similar to those gathered for terrestrial channels. In general, propagation channels are linear and time varying, but can sometimes be approximated or modeled as time-in variant. For linearly time-varying channels, the CIR or its Fourier transform, the time v arying channel transfer function (CTF), completely characterizes the channel [29]– [31], [34], [35], [39], [49]–[55], [68]. As noted, due to relative motion of the UA V , the A G channel may be stationary only for small distances [35]. Thus, stationary distance needs to be taken into account when estimating the channel statistics [31], [82], [83]. Another higher -lev el parameter that has been used by some researchers to characterize the quality of the AG propagation channel is throughput, but of course this is highly dependent upon the transmitter and recei ver implementation, and param- eters of the air interface, such as the number of antennas and the transmit po wer . Hence this measure is of limited use for assessing the A G channel itself. Similarly , for MIMO channels, beam-forming gain, div ersity , and capacity of the channel are often estimated. Some commonly reported channel characteristics for A G propagation channels are giv en in the following subsections. A. P ath Loss/Shadowing Most of the AG propagation campaigns address PL and if present, shadowing, in different scenarios. For AG channels with an LOS component, PL modeling be gins with free space propagation loss; when the earth surface reﬂection is present (not blocked or suppressed via directional antennas), path loss can be described by the well-known two-ray model. Parallel to the developments in terrestrial settings, most of the measurements employ the log-distance PL model where the loss increase with distance is indicated by the path loss exponent (PLE). In [48], PL is calculated for open ﬁeld and suburban areas for different U A V and GS heights for a small hov ering U A V . Comprehensive PL measurements in L and C bands were carried out in different propagation scenarios in [29], [31], [35], [49]–[55] as summarized in T able III. The values of PLE were found to be slightly different for urban, suburban, hilly , and over water scenarios, but are generally close to the free-space value of 2 with standard deviation around the linear ﬁt typically less than 3 dB. In [38], it was observ ed that the PLEs for IEEE 802.11 com- munications were dif ferent during U A V hovering and moving due to different orientations of the on-board U A V antennas. Therefore, antenna patterns can distort the true channel PL characteristics and remo ving their ef fect is not alw ays easy or possible. On the other hand, for the speciﬁc U A V conﬁguration used, the resulting PL model is still useful. T ypically , PL for LOS and NLOS conditions are provided separately , e.g. [84], where for the NLOS case, there is an additional small-scale (often modeled as Rayleigh) fading term, and a constant reﬂection term in addition to the LOS PL. Analogously , the LOS models for L- and C-bands can incorporate Ricean small scale effects [35]. In [85], the reported PL is described as a function of the elev ation angle between the low altitude platform and RX θ e giv en as follows: F S P L = 20 log  ∆ h sin θ e  + 20 log( f MHz ) − 27 . 55 , (1) where ∆ h = h LP − h Rx is the difference between the height of the low altitude platform and the RX on the ground. The argument ∆ h/ sin θ e is simply the link distance expressed as a function of elev ation angle. Path loss including shado wing is reported in [30], [34], [47], [48], [86], [87], where we note that in LOS cases without actual obstruction of the ﬁrst Fresnel zone, the physical (a) (b) Fig. 4: (a) The LOS signal power variation due to ground multipath propagation. The power is normalized to free space path loss, (b) Measurement scenario en vironment in [88]. mechanism causing PL variation is not actually shadowing but often small-scale effects. In [30], PL and its associated shadowing w as attributed to buildings only when the U A V was ﬂying near the ground whereas when ﬂying higher, actual shadowing was not present but variation from small-scale fading still occurred. One can also estimate losses due to “partial” shadowing by con ventional methods. For example the shado wing in [86] was found to be a function of elev ation angle, where the shadowing magnitude was estimated by using the uniform theory of diffraction. In Fig. 4(a) we show an example for the v ariation of the LOS signal power due to ground reﬂected MPCs versus the link distance d . Speciﬁcally , this is the combined effect of the LOS component and the unresolved ground reﬂection. The measurements were taken in a rural en vironment using a 10 MHz signal bandwidth. The GS height h G is 23 m. The UA V trajectory is shown in Fig. 4(b). During the measurements, the specular reﬂection point ﬁrst passed over the roof of a b uilding and then ov er open grassy ﬁelds [88]. From Fig. 4(a) we ob- serve a periodic variation of the receiv ed po wer: an attenuation of the signal by more than 10 dB is not uncommon. These signal fades will of coursed generally negati vely impact the performance of any communication system. For an increasing link distance the frequenc y of the v ariation decreases–a direct manifestation of the two-ray model. Thus in such a channel, ev en for a U A V ﬂying at a high speed a fade can easily last sev eral seconds. It is essential to note that a ground MPC may not always be present, e.g. for the case when the ground is a poorly reﬂecting ground surface, or the surface is very rough relativ e to the signal wavelength. The PL provides complete information on link attenuation, but another indirect parameter often used for channel atten- uation estimation is received signal strength (RSS). In [37], [38], [60], RSS indicator data for an AG propagation channel based on IEEE 802.11a transmissions with different antenna orientations was provided. Data on ﬂuctuations in RSS due to multipath fading from tall building reﬂections was provided in [57], where the RSS was found to decrease due to po- larization mismatch between the TX and RX antennas when the aerial vehicle made a banking turn. The accuracy of RSS values in commercial products can v ary considerably , so when these are used, care should be taken in calibration. B. Delay Dispersion The power delay proﬁle (PDP) is the ”power version” of the CIR. This can be computed ”instantaneously , ” or more traditionally , as an average over a given spatial volume (where the channel can be considered WSS). V arious A G propagation studies in dif ferent en vironments hav e measured PDPs, and via the PDP the most common estimate of the delay-domain dis- persion is estimated: the RMS-DS. Other dispersion measures such as the delay windo w or delay interval are also sometimes reported. Statistics for the RMS-DS statistic itself are often computed, e.g., in [34], mean RMS-DS values for dif ferent elev ation angles was reported. As generally expected from geometry , the RMS-DS was found to decrease as ele vation angle increases. In [48] PDPs were measured for open areas, suburban areas, and areas covered with foliage. The Saleh-V alenzuela model, originally developed for in- door channels, is sometimes used to model the PDP when MPCs appear grouped or ”clustered” in delay . This model speciﬁes the MPCs by such clusters, and the number of clus- ters is dif ferent for different environmental scenarios. PDPs were measured for different en vironments in [31], [50]–[55], and resulting RMS-DS statistics were provided. As expected, the delay spread was found to be dependent on the terrain cov er with maximum delay spread values of 4 µs for urban and suburban settings. The largest RMS-DS values generally occur when there are large b uildings that can provide strong MPC reﬂections. For hilly and mountainous terrain, maximum RMS-DS values of 1 µs for hilly regions and 180 ns for the mountainous terrain were reported. In ov er water settings, the maximum RMS-DS value reported was 350 ns . Again, in all these settings cited here, a LOS component was present between GS and U A V , hence for the majority of the time, RMS-DS was small, on the order of a fe w tens of nanoseconds. In [89], a ﬁnite-difference time domain model for the electric ﬁeld propagating at very lo w heights over sea was de veloped. An RMS delay spread model for very high frequency (VHF) to 3 GHz was presented, with RMS-DS a function of wav e height. C. Narr owband F ading Severity: Ricean K -factor Small scale amplitude fading in A G propagation channels usually follows a Ricean distribution due to the presence of a LOS component. The Ricean K -factor is deﬁned as the ratio of dominant channel component power to the power in the sum of all other received components. The K-factor is often used to characterize the A G channel amplitude fading. In [34], as generally expected, the authors found that the K - factor increased with increasing elev ation angle. The Ricean K -factor as a function of link distance was given in [47], during multiple phases of ﬂight (parking and taxiing, take off and landing, and en-route). The en-route phase showed the largest K -factor , follo wed by take off and landing, and parking and taxiing. In [62], it was observed that the K -factor will differ with different types of scattering trees: v alues of K ranging from 2 dB to 10 dB were reported. The K -factor was measured for both L-band and C-band A G propagation in [29], [35], [52], [55] for urban, suburban, hilly and mountainous settings, and also for over fresh water and sea scenarios. The mean values of K -factor for urban areas were reported to be 12 dB and 27.4 dB for L-band and C-band respectively . The mean K-factor values for hilly and mountainous terrain was reported to be 12.8 dB and 29.4 dB for L-band and C-band respecti vely , whereas for over sea settings, K -factor mean values for L-band and C-band were found to be 12.5 dB and 31.3 dB, respectiv ely . W orth pointing out is that in these “strong LOS” channels, the K -factor does not strongly depend on the GS en vironment. Also observed was that the C-band K -factor was larger than the L-band K- factor in all en vironments. This is attributable to two causes: ﬁrst, the C-band measurement signal bandwidth was larger than that of L-band, ameliorating fading, and second, for any giv en incident angle and surface roughness (e.g., ground, or ocean), as carrier frequency increases, the surface roughness with respect to the wa velength also increases, and hence incident signals are scattered in multiple directions rather than being reﬂected in a single direction (tow ard the recei ver). W ith fewer and/or weaker MPCs at the higher frequency , the K - factor is larger . D. Doppler Spread The Doppler effect is a well-known phenomenon for wire- less mobile communications. Considering A G propagation with UA Vs in a multipath environment, if we let φ i represent the angle between the aircraft velocity vector and the direction from which the i th MPC is receiv ed, the Doppler frequency shift of this i th MPC is f i d = v cos φ i λ , where v is the U A V velocity , and λ is the wa velength of the radio wav e. (W e assume here that the GS is motionless, else a more general formulation for the Doppler shift must be used.) If MPCs are receiv ed with different Doppler frequencies this phenomenon produces spectral broadening, called Doppler spread. In [10], [47], simulations were used to ﬁnd the Doppler shift and its effect on the channel at different phases of ﬂight (parking and taxiing, en-route, and take of f and landing). Doppler spread in a multipath environment implementing OFDM systems was considered in [44], where arriving MPCs were observed to have dif ferent frequency offsets. In such a case, if the receiv er CFO synchronizer cannot mitigate the effect of these different frequenc y of fsets, this results in inter- carrier interference (ICI). In [23], a mitigation technique for Doppler shift w as proposed for the case where the U A V is relaying between two communication nodes. The UA V acts as a repeater that provides the required frequency shift to mitigate the Doppler ef fect. A three dimensional AG Doppler delay spread model was provided in [22] for high scattering scenarios. Doppler spread for A G propagation is also discussed in [39], [43], [48], [49], [68]. E. Measured Air Interface Statistics Apart from the main channel characteristics, there are other performance indicators that can be measured. T wo of these are throughput and bit error ratio (BER) with particular communication technologies. As with RSSI measurements, these are useful for the particular technology and en vironment in question, but may of fer very little that is directly relev ant to modeling the A G channel. The throughput of an A G propagation channel was inv estigated in se veral studies, most commonly using the IEEE 802.11 protocol. Throughput analy- sis using dif ferent versions of the IEEE 802.11 protocol were carried out in [37], [38], for different antenna orientations, propagation distances, and U A V ele vations. A throughput analysis of IEEE 802 . 11 n was carried out in [18], where–as expected–it was found that throughput is directly dependent on the modulation and coding scheme. Throughput analysis for data relaying and ferrying for an A G propagation channel was carried out in [19]. It was observed here that mobile relaying can achieve more than twice the throughput of static relaying for a giv en delay tolerant system. Some results for BER as a function of signal-to-noise- ratio (SNR) for AG propagation channels are av ailable in the literature to compare the performance of different implemen- tation schemes. In [90], BER was measured against SNR for different modes of LD A CS1, as a function of distance and for dif ferent phases of ﬂight. A similar study was conducted in [91], where BER was measured against SNR for an ov er sea A G propagation channel with distance measuring equipment (DME) co-channel interference present. In [92], BER versus SNR analysis was performed for different ﬂight route phases for different values of Ricean K -factor . BER versus SNR analysis was performed in [46] for comparing the ef fect of presence and absence of ICI for an IEEE 802 . 11 a OFDM system in the presence of additiv e white Gaussian noise (A WGN). F . Simulations for Channel Characterization Apart from measurement campaigns for AG propagation channel modeling, some simulation based channel characteri- zations are also a vailable in the literature, where the real time en vironmental scenarios are imitated using computer simula- tions. Simulations in urban/suburban areas were performed in [45], [85], [87], [92]. The antenna considered in these en vironments was omni-directional. Different carrier frequen- cies 200 MHz, 700 MHz, 1 GHz, 2 GHz, 2 . 5 GHz, 5 GHz, and 5 . 8 GHz were covered for AG channel characterization in the urban/suburban environments, and different heights of U A Vs, ranging from 200 m to 2000 m were considered. The PL (from simulated RSS) was estimated. Over sea based channel simulations were carried out in [89], where a channel sim- ulator imitating the sea environment was developed. Carrier frequencies from 3 kHz- 3 GHz were used, with the TX and RX placed 3 . 75 m above the sea surface. The main goal of the study was to quantify sea surface shadowing for the marine communication channel using U A Vs. The channel characteris- tics of PL and root mean square-delay spread (RMS-DS) were modeled based on the sea surface height. In [91], simulations were conducted in en vironmental sce- narios consisting of over sea, hilly , and mountainous terrain. Performance of A G communications using ﬁlter bank multi- carrier (FMBC) modulation systems and LD A CS were com- pared. The results showed that FMBC has better performance than LD A Cs, especially in the presence of interference from DME signals. In the presence of the AG channel, the FBMC and LDA CS performance is comparable. Other simulations of communication systems employed over AG propagation channels, for particular simulation scenarios, are also av ailable in the literature [23], [44], [93]. In [94], the effect of the U A V height for optimal cov er- age radius was considered. It is observ ed that by adjusting U A V altitude, outage probability can be minimized: a larger ”footprint” is produced with a higher UA V altitude, b ut of course increased altitude can increase PL. An optimum U A V height is e valuated that maximizes the co verage area for a giv en SNR threshold. The Ricean K -factor was found to increase exponentially with ele vation angle between U A V and GS, given as K = c 1 exp c 2 θ , where c 1 and c 2 are constants dependent on the environment and system parameters. The relation between minimizing outage probability or maximizing cov erage area for a giv en SNR threshold is solved only based on path loss without considering the ef fect of scatterers in the en vironment. The consideration of geometry of scatterers in the analysis would of course make it more rob ust and realistic. V . UA V AG P RO PAG A T I O N M O D E L S The U A V A G propagation measurements discussed in the previous section are useful for dev eloping models for dif- ferent en vironments. In the literature, U A V A G propagation channel models ha ve been developed using deterministic or statistical approaches, or their combination. These channel models can be for narrow-band, wide-band, or ev en UWB communications. Complete channel models include both large scale and small scale effects. In this section, we categorize A G propagation channel models in the literature as shown in Fig. 5, and revie w some of the important channel models. A. AG pr opagation Channel Model T ypes T ime-variant channel models can be obtained via deter- ministic or stochastic methods or by their combination. The deterministic methods often use ray tracing (or , geometry) to estimate the CIR in a gi ven en vironment. These deter- ministic channel models can ha ve very high accuracy but require extensi ve data to characterize the real en vironment. This includes the sizes, shapes, and locations of all obstacles in the en vironment, along with the electrical properties (per- mittivity , conducti vity) of all materials. Hence such models are inherently site-speciﬁc. They also tend to require adjust- ment of parameters when comparing with measurement data. Since ray tracing based techniques employ high-frequency approximations, they are not always accurate. The y are not as accurate as full wave electromagnetic solutions, e.g., the method of moments and ﬁnite difference time domain methods for solving Maxwell equations [95], but ray tracing methods are of course far less complex than these full-wav e solutions. Such deterministic simulators are also v ery complex when they are used to model time varying channels. Ray tracing was used in [22], [59], [85], [87], [96], [97] for different fully deterministic A G propagation scenarios. The models in [31], [52], [54] are a mix of deterministic and stochastic models (sometimes termed quasi-deterministic). Speciﬁcally , the LOS and earth surface reﬂection are modeled deterministically via geometry , and the remaining MPCs are modeled stochastically , with parameter distributions (for MPC amplitude, delay , and duration) for each environment based on a large set of measurement data. Purely stochastic channel models can be obtained either from geometric and numerical analysis without using measure- ments or they can be wholly empirical. Early cellular radio channel models, e.g., the COST 207 models, are examples of the latter . These types of models are becoming less and less common over time though, as incorporation of kno wn physical information is shown to improve accuracy , and the greater model complexity is no longer prohibitiv e because of continuing advances in computer memory capacity and computational po wer . Geometric based channel models for A G propagation generally require three spatial dimensions to be accurate. The associated velocity vector for UA V motion in space also requires three dimensions, although 2D approxi- mations can often be very accurate. In order to model the scatterers around the GS, two elliptical planes intersecting a main ellipsoid were considered in [27], [28], [84], [92], where the MPCs are deﬁned by the ellipsoid and the two elliptical planes. Scatterers are considered to be randomly distributed on two spheres surrounding the TX and the RX in [26]. In [25], [41], the distribution of scatterers around the GS is modeled using a three dimensional cylinder . The geometry-based stochastic channel models (GBSCMs) can be further classiﬁed into regular shaped GBSCMs (RS- Scenario Ref. Path (LOS/NLOS) Model type PLE ( γ ) or ( α, β ) parameters Intercept P L 0 P L 0 P L 0 (dB) σ σ σ (dB) Suburban, open ﬁeld [48] LOS,OLOS log-distance PL γ : 2 . 54 − 3 . 037 21 . 9 − 34 . 9 2.79-5.3 Hilly suburban [51] LOS - − - 3.2-3.6 L-band, 1.9-3 C-band Lightly hilly rural (for h U = 120 m) for other values of the height, see T able II in the paper [98] LOS log-distance PL (alpha-beta model) α = 2 . 0 , β = − 35 . 3 - 3 . 4 Urban, suburban, rural [57] - Free space PL − - - Urban [58] - Free space PL − - - Urban [29] LOS Log-distance PL γ : 1 . 6 L-band, 1 . 9 C-band 102.3 L-band, 113.9 C-band - Urban, suburban [54] LOS Log-distance PL, two ray model γ : 1 . 7 L-band, 1 . 5 − 2 C-band 98 . 2 − 99 . 4 L-band, 110 . 4 − 116 . 7 C-band 2 . 6 − 3 . 1 L-band, 2 . 9 − 3 . 2 C-band Urban, suburban [86] LOS,NLOS Modiﬁed free space PL - - - Urban, open ﬁeld [38] LOS Log-distance PL γ : 2 . 2 − 2 . 6 - - Urban [99] LOS,NLOS Modiﬁed free space PL - - - Urban [100] - Modiﬁed LUI model - - - Urban, rural [34] LOS Log-distance PL γ : 4 . 1 - 5.24 Near airports [47] LOS Log-distance PL γ : 2 − 2 . 25 - - Open ﬁeld [60] - Log-distance PL γ : 2 . 01 - - - [61] LOS Log-distance PL γ : 2 . 32 - - Hilly , mountainous [52] LOS Log-distance PL γ : 1 . 3 − 1 . 8 L-band, 1 − 1 . 8 C-band 96 . 1 − 106 . 5 L-band, 115 . 4 − 123 . 9 C-band 3 . 2 − 3 . 9 L-band, 2 . 2 − 2 . 8 C-band Forest/foliage [62] - - - - - Over sea [35] LOS T wo ray PL - - - Over water, sea [52] LOS Log-distance PL, two ray PL γ : 1 . 9 , 1 . 9 over water and sea for L-band, 1 . 9 , 1 . 5 over water and sea for C-band 104 . 4 , 100 . 7 over water and sea for L-band, 116 . 3 , 116 . 7 over water and sea for C-band 3 . 8 − 4 . 2 over water and sea for L-band, 3 . 1 − 2 . 6 for ov er water and sea for C-band Over sea [67] LOS T wo ray PL, log distance PL, free space PL γ : . 14 − 2 . 46 19 − 129 - Ensemble of containers, see T able II in the paper [101] LOS Dual slope, − − - T ABLE V: Large scale A G propagation channel fading characteristics. GBSCMs) or irregular shaped GBSCMs (IS-GBSCMs). For RS-GBSCMs, the scatterers are assumed to be distributed on regular shapes e.g., ellipsoids, cylinders, or spheres. These models often result in closed form solutions, but are of course generally unrealistic. In contrast, the IS-GBSCM distributes the scatterers at random locations through some statistical distribution. The properties of the scatterers in both cases are generally deﬁned beforehand. In some cases, authors assume a large number of scatterers a priori, and via the Central Limit Theorem, obtain a Ricean amplitude distribution to obtain estimates of the CIR based upon some geometry . Alterna- tiv ely , signal interaction from randomly distributed scatterers can be estimated directly , or with the help of ray tracing software [85], [87], [96]. A non-geometric stochastic channel model (NGSCM) based on a Markov process is provided in [43]. The ground to air fading channel was described by a Markov process that switches between the Ricean and Loo models, dependent on the ﬂight altitude. B. P ath Loss and Large Scale F ading Models As noted, in mostly-LOS AG channels, large scale fading only occurs when the LOS path between U A V and GS gets obstructed by an object that is large relativ e to a wav elength. Some models for this attenuation mechanism exist (e.g., terrain diffraction, tree shadowing), but not much measurement data for U A V channels obstructed by buildings has been reported. When the LOS path does not get obstructed, the only other truly large-scale effect is the two-ray variation from the earth surface MPC. There are numerous measurement campaigns in the literature for PL estimation in different en vironments, as summarized in T able V. Large scale fading models in the literature cov er both the PL and shadowing. In the majority of the literature, the well-kno wn terrestrial based log-distance PL model with free space propagation path loss reference (”close-in, ” CI) is used: L C I ( d ) = P L 0 + 10 γ log 10 ( d/d 0 ) + X FS , (2) where L C I ( d ) is the model path loss as a function of distance, P L 0 is the PL at reference distance d 0 in free space gi ven by U A V A G c ha nne l m o de l s S t a t i o n a r y U A V A G c ha nne l m o de l s T i m e v a r i na t / i nv ar i a nt D e te r m i ni s ti c S to c ha s ti c F r o m e m pi r i c a l m e a s ur e m e nt s R ay tr aci ng RS - G B S M IS - G B S M F r o m g e o m e tr i c / nu m e r i c a l a na l y s i s A n al y ti c a l e . g . , 2 - r a y Fig. 5: U A V A G channel model characterization. 10 log [  4 π d 0 λ ) 2 ] , γ is the path loss exponent (PLE) obtained using minimum mean square error best ﬁt, and X FS is a random variable to account for shadowing, or in the case of LOS channels, the v ariation about the linear ﬁt. In free space the value of PLE is 2, but as seen from T able V, measured values of PLE v ary from approximately 1.5 to 4. One might conceptually divide the path between the UA V and the GS into two components: the free space component above the ground and the remaining terrestrial inﬂuenced components. When the GS antenna height is well abo ve surrounding obstacles, we e xpect the terrestrial components to hav e smaller effect and the PLE is near to that of free space. Another PL model used in the literature for large scale fading is ﬂoating intercept (FI) [102]. This model is similar to (2), b ut the free space PL at reference distance is eliminated and the model is dependent on two parameters represented as α and β [98], where α is the slope and β represents the intercept giv en as L FI ( d ) = α 10 log 10 ( d ) + β + X FI , (3) where X FI is a random variable representing the v ariation of the PL. The two PL models discussed above are based on single slope. These models hold in areas where the characteristics of the channel do not change drastically . Howe ver , in some settings with NLOS paths and complex geometries resulting in higher order reﬂections and diffractions, these single-slope models can hav e large regression errors. In such cases, a dual slope (DS) PL model is sometimes used [101], [103]. This model is similar to the FI model, but has two different slopes for different link distance ranges, and can be represented as L DS ( d ) = ( α d 1 10 log 10 ( d ) + β d 1 + X DS , d ≤ d 1 α d 1 10 log 10 ( d 1 ) + β d 1 + α d 2 10 log 10 ( d/d 1 ) + X DS , d > d 1 (4) where α d 1 , α d 2 are the slopes of the ﬁts for at two link distance ranges separated by threshold d 1 , and X DS is a random variable representing the variation in the ﬁt. PL estimates using log-distance models (2) are gi ven in [29], [37], [38], [47], [48], [52]–[54], [59]–[61], [67], [84], [89], [100], [104]. There are other PL models that consider shad- owing for NLOS paths, and additional losses incurred from other obstacles PL [58], [86], [99]. Due to the potential three dimensional motion of U A Vs, modiﬁed free space PL models accounting for UA V altitude can also be de veloped; sev eral that are a function of ele vation angle are considered in [85], [87], [105]. The two ray PL model described earlier in subsection II-C is provided in [13], [31], [49]–[51], [53]–[55], [67]. In case of two ray PL modeling, the v ariation of the PL with distance has distincti ve peaks due to destructive summation of the dominant and surface-reﬂected component. In the majority of PL models, PL variation is approximated as a log-normal random variable. This v ariation can be either due to shadowing from the UA V body (see next subsection) or from MPCs attributable to terrestrial scatterers such as buildings [13], [29], [31], [34], [47], [48], [51], [52], [54], [61], [67], [84], [86]. In [98], log-distance FI models for the path loss exponent and shado wing for the A G radio channel between airborne U A Vs and cellular networks is presented for 800 MHz and U A V heights from 1 . 5 m to 120 m abov e ground. In [101], the low altitude A G UA V wireless channel has been inv estigated for a scenario where a U A V was ﬂying abov e an ensemble of containers at 5.76 GHz. Narrow- and wideband measurements hav e been carried out. The paper presents a modiﬁed path loss model and po wer delay proﬁles. Most interesting is that in this particular environment, delay dispersion actually increases with altitude as the U A V rises above metallic structures. Another common model used in the literature [24], [106]– [111] averages the path loss over the probabilities of LOS and NLOS path loss as follows [24], [112]: PL avg = P (LOS) × PL LOS +  1 − P (LOS)  × PL NLOS , (5) where PL LOS and PL NLOS are the path loss in LOS and NLOS conditions, respectiv ely , P (LOS) denotes the proba- bility of having a LOS link between the U A V and the ground node, giv en by [24], [112]: P (LOS) = m Y n =0 " 1 − exp − [ h U − ( n +1 / 2)( h U − h G ) m +1 ] 2 2Ω 2 !# , (6) where we have m = ﬂoor( r √ ς ξ − 1) , r is the horizontal distance between the U A V and the ground node, h U and h G are as shown in Fig. 1 of this survey , ς is the ratio of built- up land area to the total land area, ξ is the mean number of buildings per unit area (in km 2 ), and Ω characterizes the height (denoted by H ) distribution of buildings, which is based on a Rayleigh distribution ( P ( H ) = ( H / Ω) 2 exp( − H / 2Ω 2 ) ). In [24], for a speciﬁc value of θ in Fig. 2 of [24], a sigmoid function is also ﬁtted to (6) for different en vironments (urban, suburban, dense urban, and highrise urban) to enable analytical tractability of U A V height optimization. Since (5) averages the path loss ov er large number of potential LOS/NLOS link possibilities, it should be used carefully if used with system- lev el analysis while calculating end metrics such as throughput and outage. Similarly , path loss v ariability should be added to the model of (5). Selection of a suitable PL model for a giv en AG propagation scenario is piv otal. In most of the literature, the PL model for of (2) is used due to its simplicity and provision of a standard platform based on reference distance free space propagation loss for comparison of measurements in different en vironments. A reference distance of 1 m is often taken as a standard for short-range systems, but larger values are also used. Howe ver , in some scenarios, where the reference free space propagation loss is not av ailable, the FI model (3) may be used. Y et due to lack of any standard physical reference, the FI slope cannot be deemed PLE and will be dependent on the environment. Additionally , the variability of the PL is generally a zero mean Gaussian random variable that has approximately similar v alues for both the CI and FI model types. A general recommendation for selection of path loss model for a given measurement scenario from T able V is as follows: for an open ﬂat or hilly area with light suburban, rural or no terrain cov er , and for ov er water , the two ray PL model or free space reference log-distance model (2) may be preferred. For open ﬂat or hilly en vironments with urban terrain cover , or for complex geometrical en vironments with longer NLOS paths, a dual slope PL (4) or free space reference log-distance PL 2 may be best. The FI model in (3) may be preferred in certain specialized en vironments e.g., [101]. In T able V, the model types denoted log-distance refer to the general log-distance equation for path loss with dif ferent reference distances and additional parameters. C. Airframe Shadowing Airframe shadowing occurs when the body of the aircraft it- self obstructs the LOS to the GS. This impairment is some what unique to A G communications, and not much exists in the liter- ature on this effect. One reason for this is that such shado wing can be largely (but not always completely) alleviated by using multiple spatially separated antennas: airframe shadowing on one antenna can be made unlikely to occur at the same time as shadowing on the other(s). In addition to frequency and antenna placement, shadowing results also depend on the exact shape, size, and material of the aircraft. For small rotorcraft, depending on frequency and antenna placement, airframe shado wing could be minimal. Example measurement results, as well as models for airframe shadowing, for a ﬁxed wing medium sized aircraft, were provided in [113]. For these results, at frequencies of 970 and 5060 MHz, wing shadowing attenuations were generally proportional to aircraft roll angle, with maximum shadowing depths exceeding 35 dB at both frequencies. Shadowing durations depend upon ﬂight maneuvers, but for long, slo w banking turns, can exceed tens of seconds. An illustration of airframe shado wing is shown in Fig. 6 where recei ved power is plotted against time for a wideband ( 50 MHz) signal in C-band before, during, and after the medium-sized aircraft made a banking turn. The receiv ed power on two aircraft antennas (denoted C1, C2), bottom mounted and separated by approximately 1 . 2 m, is shown. Attenuations due to airframe shadowing, along with the po- larization mismatch that occurs during the aircraft maneuver , exceed approximately 30 dB in this case. D. Small Scale F ading Models Small-scale fading models apply to narrow-band channels or to individual MPCs, or taps in tapped delay line wide- band models, with bandwidth up to some maximum v alue (i.e., small scale fading may not pertain to MPCs in a UWB channel). The depth of small scale amplitude fades on a gi ven signal also generally v aries in versely with signal bandwidth [114]. Stochastic fading models are obtained through analysis, empirical data, or through geometric analysis and simula- tions [25]–[27], [41], [84], [92]. As noted in subsection V -A, Ref. Scenario Time- variant/Time- in variant Modeling type Frequency spectrum DS (Hz) Fading distribution K -factor (dB) [30] Urban/Suburban Time-in variant Statistical Narrow-band - Ricean - [48] Suburban/Open ﬁeld T ime-in variant Statistical Ultra-wideband - Nakagami - [57] Suburban/Open ﬁeld Time-v ariant - - 833 - - [58] Urban/Suburban - - Narrow-band - - [34] Urban/suburban Time-in variant Statistical Wide-band - Rayleigh, Ricean - [47] Urban/suburban - Statistical W ide-band 1400 Ricean (- 5 )- 10 [54] Urban/Suburban Time-v ariant Statistical Wide-band - Ricean 12 - 27 . 4 in L and C band [68] Hilly T ime-variant - W ide-band 10000 - - [62] Forest/foliage - Statistical Ultra-wideband - Ricean, Nakagami 2 - 5 [53] Sea/fresh water T ime-variant Statistical W ide-band - Ricean 12 , 28 for L and C band [44] - T ime-variant Statistical W ide-band 5820 - - T ABLE VI: Small scale A G propagation channel fading characteristics. Fig. 6: Received power vs. time for illustration of shadowing before, during and after the banking turn of medium sized aircraft at C-band. the GBSCMs can be subdivided into RS-GBSCM and IS- GBSCM. In [92], a time-variant IS-GBSCM was provided with a Ricean distribution for small scale fading. T ime-variant RS-GBSCM were provided in [26], [41], and these also illustrated Ricean small scale fading. A NGSCM was provided in [43], where GA fading was described using Ricean and Loo models. The Loo model was deriv ed based on the assumption that the amplitude attenuation of the LOS component due to foliage in a land mobile satellite link follo ws a log-normal distribution, and that the fading due to MPCs follows a Rayleigh distribution. The switching between Ricean and Loo models was controlled by a Markov process dependent on ﬂight height. In [27], a GBSCM for MPCs was provided in the form of shape factors describing angular spread, angular compression, and direction of maximum fading using the probability density function (PDF) of angle of arriv al. T able VI provides measured small scale AG fading charac- teristics reported in the literature for various en vironments. As previously noted, the most common small scale fading distribution for A G propagation is the Ricean. As in terrestrial channels, for the NLOS case, the Rayleigh fading distribution typically provides a better ﬁt [30], [34], [42], [45], [47], [93], [115], and of course, other distributions such as the Nakagami- m and W eibull distributions might also be employed. Small scale fading rates depend upon velocity , and these rates are proportional to the Doppler spreads of the MPCs [43], [46], [47], [50], [57], [57]. E. Intermittent MPCs Another AG characteristic that may be of interest in high- ﬁdelity and long-term channel models is the intermittent nature of MPCs. From geometry , it is easy to deduce that for a giv en vehicle trajectory in some en vironment, individual MPCs will persist only for some ﬁnite span of time [31]. This has been noted in V2V channels as well, but with UA Vs and their potentially larger velocities, the intermittent MPC (IMPC) dynamics can be greater . These IMPCs arise (are ”born”) and disappear (”die”) naturally in GBSCMs. They may also be modeled using discrete time Markov chains. The IMPCs can signiﬁcantly change the CIR for some short time span, hence yielding wide variation in RMS-DS. (Another manifestation of so-called ”non-stationarity . ”) Example models for the IMPCs– their probability of occurrence, duration, delay , and amplitude– appear in [31], [35], [55]. In Fig. 7, from [13] the fading of MPCs as a function of time and delay are shown. The amplitude of MPCs generally decay with excess delay at a giv en time instant. Additionally , there is a continuous birth and death process of MPCs at different instants of time. This can be represented using CIR as [13]: h ( t, τ ) = M ( t ) − 1 X i =0 p i ( t ) a i ( t ) exp( j φ i ( t )) δ ( τ − τ i ( t )) , (7) where h ( t, τ ) is the time variant channel impulse response, M ( t ) is the total number of MPCs at time instant t , p i ( t ) represents the multipath persistence process coefﬁcient and can take binary values [0 , 1] . The amplitude, phase and delay of i th MPC at time instant t are represented as a i ( t ) , φ i ( t ) and τ i ( t ) respectiv ely . The phase term is given as φ i ( t ) = 2 π f i d ( t )( t − τ i ( t )) − f c ( t ) τ i ( t ) , where f i d ( t ) = Fig. 7: Fading and birth and death process of intermittent MPCs from [13]. v ( t ) f c ( t ) cos(Θ i ( t ) /c is the Doppler frequency of the i th MPC, Θ i ( t ) is the aggregate phase angle in the i th delay bin, c is the speed of light and f c represents the carrier frequency . The channel transfer function H ( f , t ) from (7) is then given as follows: H ( f , t ) = M ( t ) − 1 X i =0 p i ( t ) a i ( t ) exp  j 2 π f i d ( t )  t − τ i ( t )   × exp  − j 2 π f c τ i ( t )  exp  − j 2 π f τ i ( t )  , (8) The ef fect of the Doppler spread is typically negligible compared to carrier frequency at lower velocities. Therefore the carrier frequency term will dominate the variation of the transfer function. Fig. 8(a) shows a sequence of PDPs versus link distance for a near -urban A G link near Cleveland, OH. Flight parameters can be found in [54]. In this ﬁgure, the IMPCs are clearly visible, here caused by reﬂections from obstacles near the Lake Erie shoreline. In Fig. 8(b) RMS-DS vs. link distance for a hilly en vironment in Palmdale, California is shown. The intermittent nature of the MPCs produces “spikes” and “bumps” in the RMS-DS values, illustrating the potential rapid time variation of A G channels. F . MIMO A G Pr opagation Channel Models The use of MIMO systems for A G U A V communications has been gaining popularity . The rationale, increased through- put and reliability , is the same one dri ving mmW ave and future 5G research. In [116], it was shown that it is possible to attain higher spatial multiplexing gains in LOS channels by properly selecting the antenna separation and orientation as a function of carrier wa velength and link distance. This careful alignment is not always practical or possible with U A Vs, especially when mobile. The adv antages of spatial div ersity and multiplexing gains in MIMO are often only moderate due to limited scattering av ailable near UA Vs or GSs. In [117], it w as demonstrated Received power (dBm) Delay (ns) Link distance (km) RMS-DS (ns) Link distance (km) (a) (b) Fig. 8: (a) Sequence of PDPs versus link distance for a near- urban A G link near Cle veland, OH, (b) RMS-DS vs. link distance for a hilly en vironment in Palmdale, California. that due to limited spatial div ersity in the A G channel, only moderate capacity gains are possible. In order to obtain better spatial multiplexing gains, larger antenna separations are required, and this requires large antenna arrays that are not feasible on-board small U A Vs. Use of higher carrier frequen- cies makes it possible to use electrically-large antenna arrays, but higher frequencies yield higher PL (this can be mitigated somewhat by beamforming, at the expense of the complexity required for beam steering). Moreov er , accurate channel state information (CSI) is important for MIMO systems for higher performance, but in a rapidly v arying A G propagation channel, it can be difﬁcult to provide accurate CSI and hence MIMO gains can be limited. The use of MIMO on airborne platforms also incurs additional cost, computational complexity , and power consumption. There is a limited number of studies av ailable in the literature for MIMO A G propagation channel measurements. Sea Dry earth Fig. 9: Ray tracing simulation scenario for over sea scenario, where the U A V ﬂies over a straight line. In [39], a detailed measurement analysis of the AG MIMO propagation channel was provided. It was observ ed that a considerable spatial de-correlation of the received signal at the GS is achie ved due to the interaction of non-planar wa vefronts. These wav efronts are generated due to near ﬁeld effects from the measurement vehicle, on which the GS antennas were mounted. Spatial di versity from antennas located on the UA V was also observed, interestingly at higher elev ation angles. The authors suggest that ha ving scatterers near the GS can yield larger spatial div ersity . The received signal in [63] was analyzed for multiple-input-single-output (MISO) and MIMO systems, and it was observed that the use of MIMO systems enables a more robust channel for changes in antenna orientations arising from U A V maneuvering. In [65], MIMO system performance was tested in different scenarios of the outdoor en vironment, including urban, rural, open ﬁeld, and forest. The ef fect of terrain cover on the recei ved power was analyzed for these dif ferent scenarios with the result that the propagation channel in the open ﬁeld is mostly inﬂuenced by the ground reﬂections, whereas in case of forests, the reﬂection and shadowing from the trees is a major contributor to the propagation channel characteristics. In rural and urban cases, the reﬂections from the walls and surfaces of building structures play an important role. T ime-variant GBSCMs for MIMO systems pro vided in [26], [41], [92], [118] were explored through simulations. A simulation based AG MIMO channel propagation model was provided in [84] for a hilly area. The results indicate increased throughput from spatial multiplexing and higher SNR from the MIMO system in comparison to SISO, as expected. A stochas- tic model for a mobile to mobile A G MIMO propagation channel was presented in [26]. These results show that there was considerable capacity increase and reduction in outage probabilities using MIMO systems if perfect instantaneous CSI is av ailable. In [118], geometry-based simulations were conducted for a massi ve MIMO implementation for a U A V A G propagation channel. The simulation results illustrate the expected result of a signiﬁcant capacity increase when a large number of antennas is used at the GS. 2 3 4 5 6 7 8 9 10 15 20 24 (a) Link range (km) 110 120 130 140 150 Path loss (dB) Simulation data Two ray model Free space 2 3 4 5 6 7 8 9 10 15 20 24 (b) Link range (km) 100 110 120 130 Path loss (dB) Simulation data Two ray model Free space Fig. 10: Ray tracing PL results for o ver sea water settings, (a) C-band (5.03 GHz - 5.091 GHz) , (b) L-band (0.9 GHz - 1.2 GHz). 13 13.2 13.4 13.6 13.8 14.0 Link range (km) 124 126 128 130 132 134 136 138 140 Path loss (dB) Simulation data Two ray model Free space Fig. 11: Zoomed in results of PL for over sea water simulations of Fig. 10 for C-band at link distances of 13 km - 14 km. G. Ray T racing Simulations In the literature, in addition to measurements, channel characterization for A G propagation is also carried out using simulations. These simulators are either based on customized channel environments on a given software platform or can be realized using ray tracing simulations. There are PL models av ailable for these simulated en vironments [24], [85], [87], [89], [105], [115]. Urban en vironmental scenarios for LOS and NLOS paths were considered in [24], [85], [87] where log- distance and modiﬁed free space PL models were suggested. In [89] a log-distance path model was provided for LOS and NLOS paths for ov er sea settings in a simulated en vironment. Howe ver , to the best knowledge of the authors, there are no speciﬁc experimental studies av ailable in the literature that experimentally validate the channel models proposed using 200 400 600 800 1000 1200 1400 1600 1800 2000 Distance (m) 110 115 120 125 PL (dB) With scatterers Without s catterers Without scatterer and sea water Path loss with scatterers (a) 1300 1310 1320 1330 1340 1350 Distance (m) 113 114 115 116 117 118 119 120 PL (dB) With scatterers Without s catterers Without scatterer and sea water (b) Fig. 12: Path loss versus distance with/without scatterers, and without the sea surface: (a) 100 m to 2 km range, and (b) 1300 m to 1350 m range. geometrical analysis and simulations in [24], [85], [87], [89], [105], [115]. Ray tracing was used for mmW ave channel characterization for 28 GHz and 60 GHz frequency bands for U A V A G propagation in [69]. Different environments were realized, namely urban, suburban, rural and over sea. It was observed that the RSS follo ws that of the two ray model and is further affected by the presence of scatterers in the surroundings. The RMS-DS was also af fected by the presence of scatterers in the surrounding en vironment and the U A V height in the giv en en vironment. If the height of the scatterers is large with dimensions large relati ve to a wav elength, we observe higher RMS-DS for higher U A V altitudes due to multiple reﬂections from the densely distrib uted scatterers. In contrast, if the height of the scatterers is small, we have smaller RMS-DS at higher (a) Link distance (km) ( b ) Link distance (km) Path loss (dB) Path loss (dB) Fig. 13: Measurement results for PL over sea scenario from [31]: (a) C-band (5.03 GHz - 5.091 GHz), (b) L-band (0.9 GHz - 1.2 GHz). U A V heights due to fewer signiﬁcant MPCs reaching the U A V . This phenomena is veriﬁed at 28 GHz and 60 GHz, where at 60 GHz, we hav e smaller RMS-DS than at 28 GHz due to higher attenuation of MPCs. Ray tracing simulations using W ireless InSite software were carried out to estimate PL for an ov er-sea scenario as shown in Fig. 9. The channel measurement parameters were set accord- ing to [31], and the simulated PL results were compared with the measured values. Fig. 10 shows the simulated PL results. In this simulated en vironment, we have buildings as scatterers near the transmitter . Due to reﬂections and diffractions from these scatterers we observe additional ﬂuctuations on top of the two ray propagation model. The deviations are due to MPCs reﬂected and diffracted from the dif ferent-shaped scatterers at different angles. These weak MPCs reach the UA V receiv er at different link distances resulting in variations from the two ray model as sho wn in Fig. 11 at a link distance between 13 km- 14 km. Similarly in Fig. 12(a), the effect of MPCs from scatterers around the TX for link distances 100 m-2 km are shown. It can be observed that without the scatterers and seawater (with ground only), we have a perfect tw o ray PL model. Y et in the presence of the scatterers around the TX, superimposed upon this ef fect is variation from additional MPCs from the scatterers; this yields what can be modeled as a random path loss component on top of the two ray model, or in effect a small scale fading. This ef fect is of course dependent on the geometry of the scenario and will cause the path loss to v ary along the trajectory of the UA V . A similar effect at the larger link distance range of 13 km- 13 . 5 km in Fig. 12(a) can be observed in Fig. 12(b). Fig. 13 shows measured and model PL results from [31] for over -sea settings, where CE2R and FE2R stands for curved earth two ray and ﬂat earth two ray model, respecti vely . There is a good match between the ray tracing simulation results and analytical results for this over sea scenario in Fig. 10, but when comparing measurement data with simulation data, we observe more ﬂuctuations in measurements due to sev eral factors: measurement equipment v ariation, ambient noise, and in particular scattering from the rough sea surface, which is not as easily modeled with the basic ray tracing. Also plotted along with the measurement data in Fig. 13 are analytical results for free space and curved- and ﬂat-earth two ray models. The curved- and ﬂat-earth two-ray models are obtained using the speciﬁc geometry and conditions of the measurement en vironment. V I . F U T U R E R E S E A R C H A R E A S F O R A G UA V C H A N N E L M E A S U R E M E N T S A N D M O D E L S In this section we discuss limitations of currently av ailable A G channel measurements and models and their possible enhancements. W e also identify some representativ e consider- ations for future research. Our aim is to incite dev elopment of more comprehensiv e, realistic, and accurate propagation channel models for future U A V communication applications. A. Future small U A V scenarios In future scenarios small UA Vs will ﬂy in cities, across suburban areas, and over rural terrain. There are two conceptu- ally very different communication approaches for small UA Vs: the ﬁrst approach is based on centralized communications, i.e., UA Vs communicate with base stations similar to the concept of 3G and 4G cellular mobile radio. These base stations would preferably be located on ele vated positions such as towers or roof tops and ha ve antennas whose radiation patterns are optimized for serving these U A Vs. The second approach foresees direct communications among all U A Vs, similar to v ehicular communications such as ITS-G5 (intel- ligent transportation systems communications standard at 5.9 GHz). Both approaches have their pros and cons in terms of robustness, latency , and capacity; as implied, no decision has been made so far on which approach to use and only a few channel measurements have been carried out so far for both approaches. The scenarios that hav e to be considered for future prop- agation measurements should encompass urban, suburban, industrial, rural, and ev en indoor or ”quasi-conﬁned” areas such as large arenas or stadiums. Attention should be directed not only to en-route situations; even though these might be less demanding in terms of propagation conditions, strong multipath components are likely to occur due to reﬂections from smooth wet ground or bodies of water , and from large buildings with metallized window fronts. In addition we also recommend inv estigating the channel for take-of f and landing scenarios, be it on roof tops, in gardens, or in other speciﬁcally assigned areas. In these take-off and landing conditions, propagation may be unfav orable due to shadowing, strong diffraction, and rich multipath, and it is in these cases where communication must work very reliably . Moreover , we think that the propagation conditions for ﬂights that bring U A Vs intentionally close to building facades, power lines, containers, and other objects (e.g., for inspection) should also be in vestigated as such propagation may exhibit special or atypical features. B. UA V AG Pr opagation Measur ements Existing A G propagation channel measurements and models mostly apply to aeronautical communications at higher ﬂight altitudes than envisioned for small U A Vs. These smaller struc- tures have limited on-board computation capabilities, strict power limitations, and can only ﬂy at much lower altitudes, and at present, only for short durations. There is a growing demand for higher data rates, lo w latency , and high reliability for future communications, and this will be challenging for current civilian U A V architectures. Additionally , as noted in Section I, there are usually two types of communications maintained simultaneously for U A Vs: payload and CNPC. Howe ver , currently there are no standards adopted worldwide for these two types of commu- nications for UA Vs. Both can hav e their own operating bands that may or may not ov erlap. The CNPC communication links are piv otal for maintaining safety of ﬂight and any interference can be catastrophic. Standards organizations are thus working on robust loss of link procedures. Moreover , the CNPC needs to be secure and resistant to jamming and hacking attacks. The USA has dev eloped a standard, primarily for medium and lar ge aircraft [119], with standards envisioned for smaller U A Vs in the future. Future measurement campaigns should take into account not only a great variety of buildings - small and large ones, rectangular and irregularly shaped ones, industrial f acilities, halls, and towers - b ut also reﬂecting areas like bodies of water , streets, and squares, and demanding situations when a UA V lands on a terrace or the lik e. Especially for modeling the U A V -to-U A V channel, dif ferent velocities and ﬂight situations should be in vestigated, e.g., two UA Vs ﬂying tow ard each other , with one U A V near ground and the other up in the air , and swarms of U A Vs ﬂying with the same velocity . For cellular-like deplo yments, interference is lik ely to be a signiﬁcant issue that inﬂuences network planning. Thus, it would be useful to have measurements up to far distancess (and ov er different terrain). W e envisage that the U A V -to-U A V channel for small UA Vs in urban areas is as div erse as the car- to-car channel, the latter being modeled as a 2.5 dimensional channel whereas the U A V -to-U A V channel will often need to be modeled as a 3 dimensional channel. In addition to the UA V settings, there are sev eral other factors that need to be taken into account for comprehensi ve A G propagation measurements using UA Vs. One of these is the placement and orientation of antennas. The placement of antennas should be such that there is minimum shado wing and noise effect from the air-frame and motors while ﬂying. Achieving this is not always easy , and will usually be U A V - speciﬁc. The antenna orientation has been shown to result in different throughputs and RSS v alues [37], [38], [60], [61] for different ﬂight maneuvers. In order to provide better co verage during ﬂight, omni-directional antennas on both TX and RX are commonly used, especially for CNPC communications. The use of directional antennas is dependent on the speciﬁc application and coverage. When selecting U A V antenna op- tions, the mechanical viability for a given U A V type should also be taken into account e.g., a long helical antenna or yagi uda structure may be difﬁcult to mount on a ﬁxed wing aircraft compared to a horn or patch antenna. There is no ﬁxed number of antennas recommended for optimum performance, and the number of antennas will de- pend on the operating frequency , UA V size, and operational en vironments. In many experiments multiple antennas are used on U A Vs, and these may be helpful for improved cov erage and div ersity gains, but at the expense of increased computation, space, and power requirements. The ambient conditions on-board U A Vs must also be taken into account for precise measurement of any communication link characteristics (for CNPC or otherwise). These ambient conditions include noise from the motors, noise from air - craft electronics, air friction while moving, sudden air gusts, temperature variations, and outside-system interference. The latter may be particularly se vere for unlicensed bands. Another consideration with the use of unlicensed bands and commodity radios is that the adaptiv e modulation and coding algorithms employed for terrestrial networks (which often assume quasi- stationary conditions) may not work so well when directly applied to highly dynamic U A V A G propagation channels. Nearly all current day channel measurements tak e advantage of positioning information, typically from global na vigation satellite systems, with GPS being the most widely used. In addition to position information, GPS signals also provide an accurate time reference. Depending on measurement require- ments and the en visioned application, the accuracy of GPS may or may not be sufﬁcient, and this should be considered before beginning measurement campaigns. When using U A Vs in swarms, the location and mobility aware routing methods that are used for terrestrial networks may need to be adapted to account for the three dimensional mov ement of U A Vs. Similarly , route selection algorithms for mobility aw are networks will need to consider the fast varying channel conditions during U A V ﬂight. C. UA V AG Pr opagation Channel Models The UA V A G propagation literature mostly covers the modeling of PL, as described in Section V -B. As noted, and as is common for terrestrial channels, the PL models are typically provided as a function of link distance. For U A Vs there might be other models appropriate for certain cases, for example a PL model as a function of U A V altitude in a giv en setting, or ev en indoor U A V PL models for certain settings (e.g., large arenas). The most accurate UA V A G propagation channel models are of course time varying, but in some cases these can be specialized to time-in variant approximations, e.g., when a U A V is hovering above an area of static objects. In [31], [35], [52], [55], the channel is considered to be quasi stationary only for short distances, and small scale fading parameters are ev aluated o ver that stationarity interval. Additional studies of the stationarity distance should be conducted for other U A V propagation scenarios, using multiple metrics: the PDP correlation coefﬁcient, correlation matrix collinearity , spectral div ergence, and e volutionary spectrum hav e all been used, but each metric has its o wn advantages and disadv antages. Depending on en vironments, additional U A V measurement campaigns will likely result in more elaborate U A V A G propagation channel models, that may make use of MPC clusters, spatial (angular) information, and correlations among model parameters. Ultimately , deterministic and hybrid chan- nel models using GBSCM principles will likely evolv e to be the most widely used to characterize U A V A G propagation. V I I . C O N C L U D I N G R E M A R K S In this paper , we hav e provided a comprehensiv e surve y for A G propagation channels for UA Vs. The measurement cam- paigns in the literature for A G propagation were summarized, with information pro vided on the type of channel sounding signal, its center frequency , bandwidth, transmit power , U A V speed, height of U A V and GS, link distance, elev ation angle, and local GS environment characteristics. Air-ground channel statistics from the literature were also provided. V arious U A V propagation scenarios and important implementation factors for these measurements were also discussed. Large scale fading, small scale fading, MIMO channel characteristics and models, and channel simulations were all described. Finally , future research directions and challenges were discussed. W e expect that more elaborate and accurate A G propagation mea- surement campaigns and channel models will be de veloped in the future, and we hope this study will be of use in that regard. R E F E R E N C E S [1] Tractica, “Commercial drone shipments to surpass 2.6 million units annually by 2025, ” accessed: 2017-11-28. [2] Wikipedia, “General atomics MQ-9 Reaper , ” accessed: 2017-07-03. [Online]. A vailable: https://en.wikipedia.org/wiki/General Atomics MQ- 9 Reaper [3] Federal A viation Administration, “F AA small unmanned aircraft regulations, ” accessed: 2017-07-03. [Online]. 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A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

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