Simultaneous Harvest-and-Transmit Ambient Backscatter Communications under Rayleigh Fading

A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 1 Simultaneous Harv est-and-T ransmit Ambient Backscatter Communications under Rayleigh F ading Furqan Jameel, T apani Ristaniemi, Imran Khan, and Byung Moo Lee Abstract Ambient backscatter communications is an emerging paradigm and a k ey enabler for perv asi ve connecti vity of lo w-po wered wireless de vices. It is primarily beneficial in the Internet of things (IoT) and the situations where computing and connectivity capabilities expand to sensors and miniature devices that exchange data on a low power budget. The premise of the ambient backscatter communication is to build a network of devices capable of operating in a battery-free manner by means of smart networking, radio frequency (RF) energy harvesting and power management at the granularity of individual bits and instructions. Due to this innov ation in communication methods, it is essential to in vestigat e the performance of these devices under practical constraints. T o do so, this article formulates a model for wireless-powered ambient backscatter de vices and deriv es a closed-form expression of outage probability under Rayleigh fading. Based on this expression, the article provides the power -splitting factor that balances the tradeof f between energy harvesting and achiev able data rate. Our results also shed light on the complex interplay of a power-splitting factor , amount of harvested energy , and the achiev able data rates. Index T erms Ambient backscatter communications, Energy harv esting, Internet of Things (IoT), Smart networking, W ireless-powered communication I . I N T RO D U C T I O N The grand vision of the Internet of things (IoT) is quickly turning into reality by bringing ev erything to the Internet [1], [2]. Latest devices ranging from smartphones to implantable sensors and wearables are claiming to be “IoT capable”. Although significant improvements hav e been seen from the design perspecti ve of wireless devices, the objectiv e of connecting everything to the Internet is still a far cry [3]. It is because se veral important challenges arise when ensuring ubiquitous connectivity of devices. As indicated in [4], one of the first challenge is the limited life-cycle of miniature wireless devices. The ener gy constrained nature of devices becomes an obstacle as the massive amount of data is transferred across an IoT network and the devices are required to be operated in an untethered manner . Then, there is a requirement of communication reliability which is ev en more difficult to maintain in large-scale wireless systems [5]. The increased reliability most often comes at a cost of increased energy consumption which cannot be regulated by small energy reservoirs of miniature IoT devices. Above all, these devices would need to demonstrate services like ultra-reliable low-latenc y communications (URLLC), enhanced mobile broadband (eMBB), and massiv e machine type communications (mMTC)for beyond 5G networks. Resultantly , it has become evident that an ultra low-po wered communication paradigm is essential for enabling short-range communication among devices, without compromising the reliability of communications [2], [6]. Of late, backscatter communication has gathered the attention of the researchers as a key enabling technology for connecting IoT devices. Backscatter communication allows radio device to transmit their data by reflecting and modulating an incident radio frequency (RF) signal. It adapts the antenna impedance mismatch in order to change the reflection coefficient. Using the receiv ed RF energy , backscatter devices harvest a fraction of energy for circuit operations [7]. It is worth highlighting that the backscatter devices do not require oscillators for generating carrier signals as they get the carrier wa ves from the dedicated RF source. In fact, the ultra-low power nature of a backscatter transmitter (i.e., below 1 mW [8]), shows promise for a very long life-cycle (i.e., 10 years) with an on-chip battery . Since the harvested ener gy from an RF source typically ranges from 1mW to 10s of mW , the low po wer consumption of backscatter devices is a perfect match for RF energy harvesting [9]. Besides the ob vious adv antages of con ventional backscatter communications, there are fe w limitations of these de vices. The backscatter de vices require a dedicated RF source for transmission of carrier wav es. Even though this model has been adopted in radio frequency identification (RFID) tags used in libraries and grocery stores, the power budget of these communication models may not be suitable for energy constrained IoT devices [6], [10]. Additionally , the centralized nature of these communication models is also a hurdle in paving the way for large-scale deployment of IoT networks. The distributed architecture of IoT networks fa vors the deployment of decentralized RF sources that can be accessed anytime. Besides this, energy harvesting through wireless power transmission can extend the life cycle of the IoT networks with little changes in hardware implementations [11], [12] T o overcome the abov e-mentioned limitations, a ne w backscatter paradigm has emerged that is called ambient backscatter communication [13]. An ambient backscatter transmitter uses ambient RF signals in order to perform in a battery-free manner . More specifically , the ambient RF signals are used for backscattering and ener gy harvesting. This flexibility allows the cost- effecti ve deployment of ambient backscatter devices while av oiding dependence on a particular RF source [14]. Howe ver , owing to the nov elty of the technology , the study of ambient backscatter communications is still at its nascent stage. A variety A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 2 of network challenges and data communication issues arise that require further exploration. Furthermore, limited theoretical knowledge of ambient backscatter communication demands new dimensions for performance ev aluation of the network. Motiv ated by the aforementioned observ ations, we perform the analysis of backscatter communication under Rayleigh fading. Specifically , our contribution is two-fold: • Deriv ation of closed-form expression of outage probability for wireless-powered devices operating under Rayleigh fading. • Deriv ation of the power-splitting factor that balances the tradeoff between energy harvesting and achiev able data rate. The remainder of the paper is org anized as follows. Section 2 discusses the related work on con ventional backscatter and ambient backscatter communications. In Section 3, a detailed description of the system model is pro vided. Section 4 pro vides the performance analysis while Section 5 discusses the numerical results. Finally , Section 6 provides key findings and conclusions. I I . R E L A T E D W O R K Backscatter communication has been considered from different aspects in wireless networks [15]. The authors of [16] employed backscatter communication to enable device-to-de vice communications. Besides this, se veral detection schemes for backscatter communication systems are proposed in [17], [18], [19]. A detector that does not require the channel state information (CSI) was constructed using a differential encoder in [18]. Specifically , they developed a model and deriv ed optimal detection and minimum bit-error-rate (BER) thresholds. Moreov er , the expressions for lower and upper bounds on BER were also derived that were corroborated through simulation results. A joint-energy detection scheme is proposed in [19] that requires only channel variances rather than specific CSI. The same authors provided a study of BER computation, optimal and suboptimal detection, and blind parameter acquisition. The non-coherent signal detection outperformed the con ventional techniques in terms of detection accurac y and computation comple xity . A successiv e interference cancellation (SIC) based detector and a maximum-likelihood (ML) detector with known CSI are presented in [17], to recov er signals not only from readers but also from RF sources. In addition to this, the authors derived BER expressions for the ML detector . It was shown that the backscatter signal can significantly enhance the performance of the ML detector as compared to conv entional single-input-multiple-output (SIMO) systems. Capacity and outage performance analysis for ambient backscatter communication systems was studied in [20], [21], [22], [23]. The authors of [20] analyzed the channel capacity over orthogonal frequency division multiplexing (OFDM) signals. The ergodic capacity optimization problem at the reader with SIC was inv estigated by the authors of [21]. Specifically , the authors jointly considered the transmit source power and the reflection coefficient and improved the ergodic capacity . For ambient backscatter communication systems, the BER of an energy detector was deriv ed and the BER-based outage probability was obtained in [22]. Zhao et al . in [23], the effecti ve distribution of signal-to-noise ratio (SNR) was deri ved and the SNR-based outage probability was e v aluated ov er real Gaussian channels. More recently , the authors in [24] in vestigated a cogniti ve radio network having ambient backscatter communication. In particular , it was considered that a wireless-powered secondary user can either harvest energy or adopt ambient backscattering from a primary user on transmission. A time allocation problem was dev eloped in order to maximize the throughput of the secondary user and to obtain the optimal time ratio between energy harvesting and ambient backscattering. Reference [25] introduced a hybrid backscatter communication scheme as an alternative access scheme for a wireless-powered transmitter . Specifically , when the ambient RF signals were not sufficient to support wireless-powered communications, the transmitter can choose between bistatic backscattering or ambient backscattering based on a dedicated carrier emitter . A throughput maximization problem was formulated to find the optimal time allocation for the hybrid backscatter communication operation. Both [24] and [25] studied a deterministic scenarios. I I I . E X P E R I M E N T A L S Y S T E M M O D E L D E S I G N Let us consider an IoT network that consists of N number of ambient backscatter devices. These ambient backscatter de vices are considered to be powered by ambient RF source. This consideration is under the assumption that ambient RF sources (like Radio signals, TV signals and WiFi signals) are abundant in the en vironment. These backscatter devices use the harvested energy from the ambient RF signals and transmit their data to the gateway as shown in Figure 1. A typical ambient backscatter device has three major operations, i.e., spectrum sensing, energy harvesting, and data exchange. Inspired by [6], we consider the circuit model of an ambient backscatter device is shown in Figure 2. Here, the main purpose of the spectrum sensor is to detect suitable ambient RF signals, whereas, the energy harvesting circuit enables the backscatter devices to operate a self-sustainable manner . This self-sustainability is essential for IoT networks as they are e xpected to operate with minimum human intervention. When the de vice is in operation mode, the spectrum sensing is performed in order to detect RF signal with large power . Afterward, the detected signal is employed for either backscatter communication or ener gy harvesting. The analog-to-digital conv erter (ADC) uses the harvested energy and con v ert it into direct current that is utilized by other modules including a microcontroller . The microcontroller performs multiple communication operation including processing the information and matching the impedance of antenna for better reception of RF signals. W e consider that the amount of energy consumed by ener gy harvester is negligible [6] and satisfies the following condition A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 3 G G G G B B B B G B A mbi ent Backscatter De vic e G Gat ewa y Fig. 1: System model. E h ≥ E b + E s + E m . (1) In the abov e expression E h , E b , E s , E m denotes the harvested energy , ener gy consumed for backscatter communication, energy consumed for spectrum sensing and the energy consumed by micro-controller/ sensor for data gather and processing. Some of the key symbols used throughout this paper are pro vided in T able I. Symbol Definition E h Harvested energy E b Energy consumed for backscatter communication E s Energy consumed for spectrum sensing E m Energy consumed by micro-controller/ sensor α Compressiv e sensing duration ρ Power -splitting factor β Reflection coef ficient of the backscatter devices θ Path loss exponent N 0 A WGN v ariance η Energy con version efficienc y M Number of wideband signals e Energy consumed for each sample ϕ Threshold of required data rate ψ Energy threshold for operation of the backscatter device f Sampling rate P b Amount of circuit power consumed during backscattering T ABLE I: Common symbols used in the article. W e now characterize the energies harvested and consumed during one time slot. W e consider that compressiv e sensing is performed in each time slot. Thus, the time slot T is divided into phases, i.e., compressiv e sensing duration (denoted as α ) and energy harvesting/ backscattering duration (denoted as ( 1 − α ) ). After compressiv e sensing, the received signal at the device is divided into two streams of power . The first part is used for energy harvesting while the other part is used for performing backscattering operation. This separation is performed with a factor ρ , where 0 < ρ ≤ 1 . A graphical representation of an interplay of ρ and α is provided in Figure 3. Assuming that an i -th backscatter device detects an ambient RF , then the recei ved signal at the device is giv en as A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 4 Ene r gy Har v es t er Micr o - c ontr oll e r P ow e r - sp lit t e r Spe c tr um Sen sing P o w er R eceiv ed Signal P o w er Ba c k sc a t t er P o w er H ar v es t ed Ener gy 𝜌 ( 1 − 𝜌 ) P o w er f or Cir c uit Oper a ti ons P o w er f or Sen sing Da t a V ari able Imp ed ance Da t a P ack e ts Sen sor Fig. 2: Circuit design of the ambient backscatter device. 𝑻 𝟏 = 𝑻𝜶 𝑻 𝟐 = 𝑻 ( 𝟏 − 𝜶 ) Back sc a t t er de v ice perf orm s c ompr ess iv e sensi ng t o sense the c han nel Back sc a t t er de v ices uses the fr action of r ecei v ed si gn al po w er ( 𝜌 ) f or har v es ting the en er gy Bac k sc a t t er de vi ces uses the fr action of r eceiv ed si gn al po w er ( 1 − 𝜌 ) f or bac k sc a t t eri ng the sensor da t a t o g a t e w a y Single time block ( 𝑇 ) Fig. 3: Time schedule and power splitting. y i, 1 = s β P P l, 1 h i, 1 s 1 + n i, 1 , (2) where y i, 1 is the receiv ed signal, s 1 denotes the normalized signal, P represents the transmit power , and P l, 1 = d θ 1 is the path loss experienced by the backscatter device and θ is the path loss exponent. Furthermore, h i, 1 represents the channel gain between the ambient RF source and backscatter device which is assumed to be Rayleigh faded, n i, 1 is the zero mean additiv e white Gaussian noise (A WGN) with N 0 variance while β is the reflection coefficient of the backscatter devices. The harvested energy is then denoted as E h,i = ρη (1 − α ) T β Ω 1 | h i, 1 | 2 P l, 1 , (3) where Ω 1 = P N 0 , ρ represents the fraction of po wer used for energy harvesting, η is the energy con version efficienc y that is considered to be same for all the backscatter devices as the y employ same circuitry . A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 5 The amount of energy consumed by the compressive sensing module is a linear multiplication of the number of samples and sampling rate. More specifically , it can be represented as E s = αf M eT , (4) where M is the number of wideband signals that hav e been detected during the phase of spectrum sensing, f is the sampling rate, and e is the energy consumed for each sample. The amount of energy consumed the backscattering module can be represented in terms of circuit po wer as E b = (1 − α ) P b T , (5) where P b is the amount of circuit po wer consumed during backscattering phase. For the sake of simplicity and without loss of generality , we consider that the power consumed by micro-controller is fixed. As a result of backscattering, the receiv ed message at the gatew ay can be written as y i, 2 = s (1 − ρ ) β P b P l, 2 h i, 2 s i, 2 + n i, 2 , (6) where y i, 2 is the receiv ed signal at the gate way , s i, 2 denotes the normalized signal sent by the i -th backscattering de vice, P represents the transmit power , and P l, 2 = d θ 2 is the path loss between backscatter de vice and the gate way . Furthermore, h i, 2 represents the Rayleigh faded channel gain between the backscatter de vice and the gatew ay , n i, 2 is the zero mean A WGN with zero mean and N 0 variance. I V . P E R F O R M A N C E A NA LY S I S A N D M E T H O D O L O G Y In this section, we deriv e the communication outage and power shortage probabilities of the backscatter devices. Based on these probabilities, we aim to find the balancing value of the ρ . A. Outage P erformance Using the Shannon capacity formula, the achiev able sum rate at the gateway can be written as R sum = N X i =1 R i , (7) where R i is the achiev able rate of i -th backscattering de vice which is gi ven as R i = (1 − α ) B T log 2  1 + (1 − ρ ) β Ω 2 | h i, 2 | 2 P l, 2  , (8) where Ω 2 = P b N 0 . The outage probability of achiev able rate can occur due to the following two conditions 1) If the harvested energy is below the energy required for operations of backscatter device. 2) If the achiev able rate is below the required rate at the gatew ay . Thus, using the total probability theorem, the outage probability can be written as P out = Pr( R i < ϕ | E h,i < ψ ) Pr( E h,i < ψ ) + Pr( R i < ϕ | E h,i > ψ ) Pr( E h,i > ψ ) , (9) where ϕ represents the threshold of required data rate and ψ = E b + E s + E m is the energy threshold for operation of the backscatter device. From the abov e equation, we note that if the harvested energy is below the threshold, then the backscatter device would not be able to transfer any data to the gate way . In this case, the probability that the rate falls below a required threshold would always be 1. Thus, we can write Pr( R i < ϕ | E h,i < ψ ) = 1 . (10) The probability that the harvested energy would fall below a specified threshold can be written as A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 6 Pr( E h,i < ψ ) = Pr  ρη (1 − α ) T β Ω 1 | h i, 1 | 2 P l, 1 < ψ  . (11) After some simplifications, it can be represented as Pr( E h,i < ψ ) = Pr  | h i, 1 | 2 < P l, 1 ψ ρη (1 − α ) T β Ω 1  = 1 − exp  − P l, 1 ψ ¯ γ 1 ρη (1 − α ) T β Ω 1  . (12) In contrast, the probability of energy harvesting increasing beyond the threshold can be represented as Pr( E h,i > ψ ) = exp  − P l, 1 ψ ¯ γ 1 ρη (1 − α ) T β Ω 1  , (13) where ¯ γ 1 is the average channel gain between RF source and the backscattering device. Let us now consider the case when the harvested energy is greater than ψ . In this case, the probability that the achie vable data rate falls below a pre-determined threshold can be written as Pr( R i < ϕ | E h,i > ψ ) = Pr  (1 − α ) B T × log 2  1 + (1 − ρ ) β Ω 2 | h i, 2 | 2 P l, 2  < ϕ | E h,i > ψ  . (14) After some straightforward simplifications, we obtain Pr( R i < ϕ | E h,i > ψ ) = Pr | h i, 2 | 2 < P l, 2 (2 ϕ (1 − α ) BT − 1) (1 − ρ ) β Ω 2 ! = 1 − exp ( − P l, 2 (2 ϕ (1 − α ) BT − 1) ¯ γ 2 (1 − ρ ) β Ω 2 ) , (15) where ¯ γ 2 is the av erage channel gain between backscattering device and the gateway . Substituting the Eqs (10), (12), (13), and (15) in (9), we obtain P out = 1 − exp  − P l, 1 ψ ¯ γ 1 ρη (1 − α ) T β Ω 1  + exp  − P l, 1 ψ ¯ γ 1 ρη (1 − α ) T β Ω 1  × " 1 − exp ( − P l, 2 (2 ϕ (1 − α ) BT − 1) ¯ γ 2 (1 − ρ ) β Ω 2 )# . (16) After solving 16, we hav e P out = 1 − exp  − P l, 2 (2 ϕ (1 − α ) BT − 1) ¯ γ 2 (1 − ρ ) β Ω 2 − P l, 1 ψ ¯ γ 1 ρη (1 − α ) T β Ω 1  . (17) B. Balancing communication outage and power shortage In this section, we aim to find the v alues of ρ that balances the tradeof f between communication outage and power shortage. In particular, we note that different values of ρ have a different impact on communication outage and power shortage. From (3), we can observe that the amount of energy harvested is the increasing function of ρ . In other words, as the value of ρ increases, the amount of harvested energy also increases, whereas, it decreases with a decrease in the value of ρ . In contrast, the achiev able rate of any i -th backscattering device is a decreasing function of ρ . Since the achie v able rate is dependent on the receiv ed SNR, therefore, increasing the value of ρ results in increasing the SNR while a reduction in ρ causes an increase in the values of SNR which in turn increases the achie v able rate. A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 7 From the above arguments, we can observe that the balancing value of ρ can be found by solving the energy harvesting and SNR expressions simultaneously . Thus, we can write ρη (1 − α ) T β Ω 1 | h i, 1 | 2 P l, 1 = (1 − ρ ) β Ω 2 | h i, 2 | 2 P l, 2 . (18) After cross multiplication and taking least common multiple, we obtain the ρ ∗ as ρ ∗ = | h i, 2 | 2 Ω 2 P l, 1 | h i, 2 | 2 Ω 2 P l, 1 + η (1 − α ) T β Ω 1 | h i, 1 | 2 P l, 2 . (19) From the above expression, we can observe that ρ ∗ is in versely proportional to the Ω 1 . Moreo ver , if P l, 1 = P l, 2 , then the balancing value of ρ ∗ is halved. W e also note that the value of ρ ∗ increases with an increase in α indicating the direct relationship between ρ ∗ and α . V . R E S U LT S A N D D I S C U S S I O N S In this section, we provide results and relev ant discussion on the above-mentioned analysis. Unless mentioned otherwise, following parameters have been used for generating simulation and analytical results: η = 0 . 5 , B = 1 M H z , β = 0 . 5 , d 1 = d 2 = 5m, ϕ = 2kbps, θ = 2 , and ρ = 0 . 3 . Figure 4 illustrates the outage probability as a function of increasing values of SNR. It can be seen that the outage probability decreases with an increase in the SNR. Howe v er , the impact of α on P out is different for different values of SNR. Specifically , we observe that an increase in the α results in an increase in the outage probability . It is because with an increase in α provides more time for compressiv e sensing and less time for energy harvesting and backscattering. On the other hand, an increase in ρ causes an increase in outage probability . This result is caused by allocating more fraction of receiv ed power for energy harvesting and less for performing backscatter communications. In addition, the simulation results closely follow the analytical curves which indicates the validity of our theoretical model. SNR (dB ) -20 -15 -10 -5 0 5 P out 10 -2 10 -1 10 0 α = 0.1 α = 0.3 α = 0.5 α = 0.8 sim ulation ρ = 0.9 ρ = 0.3 Fig. 4: Outage probability as a function of SNR. Figure 5 (a) shows the achie v able rate as a function of increased SNR. As anticipated by the analytical e xpression, the increase in SNR improv es the achiev able rate. Howe ver , an increase in α decreases the achiev able rate. In fact, the impact of α becomes more prominent at higher v alues of SNR sho wing a rapid rise in the curves. Where Figure 5 (a) is plotted for d 1 = d 2 = 5 m, the curves of Figure 5 (b) are plotted against d 1 = d 2 = 10 m. This increase in distance has a critical impact on the achiev able rate. In particular , for the same values of SNR and α (e.g., SNR=0 dB and α =0.1), the achiev able rate drops from 20 kbps to 5 kbps when the distance is increased. Figure 6 plots the harvested energy against increasing v alues of d 1 . Indeed, this results highlight the significance of distance between the RF source and the backscatter device. It can be seen that an increase in d 1 results in decreasing the harvested A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 8 SNR (dB ) -20 -15 -10 -5 0 5 Rate (kb ps) 0 10 20 30 40 50 60 α = 0.1 α = 0.3 α = 0.5 α = 0.8 (a) SNR (dB ) -20 -15 -10 -5 0 5 Rate (kb ps) 0 5 10 15 α = 0.1 α = 0.3 α = 0.5 α = 0.8 (b) Fig. 5: Achievable rate against different values of α where (a) d 1 = d 2 = 5 m, (b) d 1 = d 2 = 10 m. A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 9 energy . Additionally , the increasing values of α decrease the harvested amount of energy due to compressiv e sensing. This decrease in harvested ener gy , against different values of α , is less prominent when Ω 1 = 5 dB. This indicates that the time scheduling is more effecti ve for large transmit power of the ambient RF source. Furthermore, this increase in Ω 1 allows devices to harvest power up to a significantly lar ger distance which influences the life-cycle of de vices. d 1 (m) 1 1.5 2 2.5 3 3.5 4 4.5 5 Harvested Ene rgy ( µ W s) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 α = 0.3 α = 0.5 α = 0.8 Ω 1 =10 dB Ω 1 =5 dB Fig. 6: Harvested energy against increasing values of d 1 . Figure 7 (a) demonstrates the tradeoff between harvested energy and achie vable rate. W e hav e plotted different curves of achiev able rate and harvested energy against the increasing values of ρ . It can be observed that an increase in ρ causes an increase in the amount of harvested energy while simultaneously reducing the achie vable rate. Since the value of α influences both rate and harvested energy , the lower values of α decreases the con v erging point of the curves of rate and energy curves. Similar trends can be sho wn in 7 (b), ho wev er , the con v erging point of the curves now shift towards the right-hand side while reducing both the harvested energy and rate. This trend can be attributed to the increase in d 1 and d 2 . This shift in balancing point sho ws that a higher value of ρ is required with an increase in distance. This also indicates that ener gy harvesting becomes a critical factor when the distance is increased between ambient RF source and the device and that between device and gatew ay . V I . C O N C L U S I O N Ambient backscatter communications provide virtually endless opportunities to connect wireless de vices. W e anticipate that wearable devices, connected homes, industrial internet, and miniature embeddable are some of the areas where ambient backscatter communications would be adapted to provide pervasi ve connecti vity . Thus, to better analyze the utility of these low- powered devices, this article has provided a comprehensive analysis of ambient backscattering model from the perspective of achiev able data rates and the amount of harvested energy . In addition to deriving closed-form expressions of outage probability and balancing po wer-splitting factor , we have shown that the distance between ambient RF source and the device plays a critical role in determining the life-cycle of de vices and the outage probability at the gatew ay . In fact, we have demonstrated that an increase in distance shifts the balancing power-splitting point to the right-hand side. Besides this, we hav e observed that when the distance is increased from 5m to 10m against fixed values of SNR and α , the achiev able rate at gatew ay drops from 20 kbps to 5 kbps. These results can act as a fundamental b uilding block for designing and large-scale deployment of ambient backscatter devices in the future. A C K N O W L E D G M E N T R E F E R E N C E S [1] E. Siow , T . T iropanis, and W . Hall, “ Analytics for the internet of things: A surve y , ” ACM Computing Surveys (CSUR) , v ol. 51, no. 4, p. 74, 2018. [2] P . Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V . C. Leung, and Y . L. Guan, “Wireless energy harvesting for the internet of things, ” IEEE Communications Magazine , vol. 53, no. 6, pp. 102–108, 2015. [3] F . Jameel, S. Kumar, Z. Chang, T . Hamalainan, and T . Ristaniemi, “Operator re venue analysis for device-to-de vice communications overlaying cellular network, ” in 2018 IEEE Conference on Standards for Communications and Networking (CSCN) , Oct 2018, pp. 1–6. A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 10 ρ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Rate (kbps) 0 50 100 Harvested Energy ( µ Ws) 0 0.01 0.02 α = 0.3 α = 0.5 α = 0.8 (a) ρ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Rate (kbps) 0 5 10 15 Harvested Energy ( µ Ws) × 10 -3 0 2 4 6 α = 0.3 α = 0.5 α = 0.8 (b) Fig. 7: Achievable rate and harvested energy versus increasing values of ρ , where η = 0 . 3 and (a) d 1 = d 2 = 5 m, (b) d 1 = d 2 = 10 m. A CCEPTED IN EURASIP JOURNAL ON WIRELESS COMMUNICA TIONS AND NETWORKING 11 [4] D. Munir, S. T . Shah, K. W . Choi, T .-J. Lee, and M. Y . Chung, “Performance analysis of wireless-powered cognitiv e radio networks with ambient backscatter , ” EURASIP Journal on W ir eless Communications and Networking , v ol. 2019, no. 1, p. 45, Feb 2019. [Online]. A v ailable: https://doi.org/10.1186/s13638- 019- 1367- 7 [5] F . Jameel, Z. Hamid, F . Jabeen, S. Zeadally , and M. A. Jav ed, “ A survey of device-to-device communications: Research issues and challenges, ” IEEE Communications Surve ys T utorials , vol. 20, no. 3, pp. 2133–2168, thirdquarter 2018. [6] X. Liu, Y . Gao, and F . Hu, “Optimal time scheduling scheme for wireless po wered ambient backscatter communications in iot networks, ” IEEE Internet of Things J ournal , pp. 1–1, 2019. [7] C. Boyer and S. Roy , “Backscatter Communication and RFID: Coding, Energy , and MIMO Analysis, ” IEEE T ransactions on Communications , vol. 62, no. 3, pp. 770–785, 2014. [8] B. K ellogg, A. Parks, S. Gollakota, J. R. Smith, and D. W etherall, “W i-Fi backscatter: Internet connectivity for RF-po wered devices, ” in ACM SIGCOMM Computer Communication Re view , vol. 44, no. 4. A CM, 2014, pp. 607–618. [9] X. Lu, P . W ang, D. Niyato, D. I. Kim, and Z. Han, “W ireless networks with RF energy harvesting: A contemporary survey, ” IEEE Communications Surveys & T utorials , vol. 17, no. 2, pp. 757–789, 2015. [10] F . Jameel, Z. Chang, and T . Ristaniemi, “Intercept probability analysis of wireless powered relay system in kappa-mu fading, ” in 2018 IEEE 87th V ehicular T echnology Conference (VTC Spring) , June 2018, pp. 1–6. [11] N. Zhao, S. Zhang, F . R. Y u, Y . Chen, A. Nallanathan, and V . C. Leung, “Exploiting interference for ener gy harvesting: A survey , research issues, and challenges, ” IEEE Access , v ol. 5, pp. 10 403–10 421, 2017. [12] Z. Chang, Z. W ang, X. Guo, C. Y ang, Z. Han, and T . Ristaniemi, “Distributed resource allocation for energy efficiency in ofdma multicell networks with wireless po wer transfer , ” IEEE Journal on Selected Areas in Communications , vol. 37, no. 2, pp. 345–356, 2019. [13] V . Liu, A. Parks, V . T alla, S. Gollakota, D. W etherall, and J. R. Smith, “Ambient backscatter: wireless communication out of thin air, ” in ACM SIGCOMM Computer Communication Re view , vol. 43, no. 4. A CM, 2013, pp. 39–50. [14] K. Han and K. Huang, “W irelessly powered backscatter communication networks: Modeling, cov erage, and capacity, ” IEEE T ransactions on W ir eless Communications , vol. 16, no. 4, pp. 2548–2561, 2017. [15] N. V an Huynh, D. T . Hoang, X. Lu, D. Niyato, P . W ang, and D. I. Kim, “Ambient backscatter communications: A contemporary survey, ” IEEE Communications Surve ys & Tutorials , 2018. [16] X. Lu, H. Jiang, D. Niyato, D. I. Kim, and Z. Han, “W ireless-powered device-to-de vice communications with ambient backscattering: Performance modeling and analysis, ” IEEE Tr ansactions on Wir eless Communications , vol. 17, no. 3, pp. 1528–1544, 2018. [17] G. Y ang, Y .-C. Liang, and Q. Zhang, “Cooperative receiv er for ambient backscatter communications with multiple antennas, ” in IEEE International Confer ence on Communications (ICC) . IEEE, 2017, pp. 1–6. [18] G. W ang, F . Gao, R. Fan, and C. T ellambura, “Ambient backscatter communication systems: Detection and performance analysis, ” IEEE T ransactions on Communications , v ol. 64, no. 11, pp. 4836–4846, 2016. [19] J. Qian, F . Gao, G. W ang, S. Jin, and H. Zhu, “Noncoherent detections for ambient backscatter system, ” IEEE T ransactions on W ir eless Communications , vol. 16, no. 3, pp. 1412–1422, 2017. [20] D. Darsena, G. Gelli, and F . V erde, “Modeling and performance analysis of wireless networks with ambient backscatter de vices, ” IEEE T ransactions on Communications , vol. 65, no. 4, pp. 1797–1814, 2017. [21] X. Kang, Y .-C. Liang, and J. Y ang, “Riding on the primary: A ne w spectrum sharing paradigm for wireless-po wered IoT devices, ” in IEEE International Confer ence on Communications (ICC) . IEEE, 2017, pp. 1–6. [22] Y . Zhang, J. Qian, F . Gao, and G. W ang, “Outage probability for ambient backscatter system with real source, ” in IEEE 18th International W orkshop on Signal Pr ocessing Advances in W ireless Communications (SP A WC) . IEEE, 2017, pp. 1–5. [23] W . Zhao, G. W ang, S. Atapattu, C. T ellambura, and H. Guan, “Outage analysis of ambient backscatter communication systems, ” IEEE Communications Letters , 2018. [24] D. T . Hoang, D. Niyato, P . W ang, D. I. Kim, and L. B. Le, “Overlay RF-powered backscatter cognitive radio networks: A game theoretic approach, ” in Communications (ICC), 2017 IEEE International Confer ence on . IEEE, 2017, pp. 1–6. [25] S. H. Kim and D. I. Kim, “Hybrid backscatter communication for wireless-powered heterogeneous networks, ” IEEE T ransactions on W ir eless Communications , vol. 16, no. 10, pp. 6557–6570, 2017. Furqan Jameel received his BS in Electrical Engineering (under ICT R&D funded Program) in 2013 from the Lahore Campus of COMSA TS Institute of Information T echnology (CIIT), Pakistan. In 2017, he received his Master’ s degree in Electrical Engineering (funded by prestigious Higher Education Commission Scholarship) at the Islamabad Campus of CIIT . In 2018, he visited Simula Research Laboratory , Oslo, Norway . Currently , he is a researcher at the Univ ersity of Jyv ¨ askyl ¨ a, Finland. His research interests include modeling and performance enhancement of vehicular networks, physical layer security , ambient backscatter communications, and wireless power transfer . He was a recipient of the Outstanding Reviewer A ward in 2017 from Elsevier . T apani Ristaniemi recei ved the M.Sc. degree in mathematics in 1995, the Ph.Lic. degree in applied mathematics in 1997, and the Ph.D. in wireless communications in 2000 from the University of Jyvskyl, Jyvaskyla, Finland. In 2001, he was appointed as a Professor with the Department of Mathematical Information T echnology , Uni versity of Jyv askyla. In 2004, he mov ed to the Department of Communications Engineering, T ampere University of T echnology , T ampere, Finland, where he was appointed as a Professor in wireless communications. Prof. Ristaniemi is currently a Consultant and a member of the Board of Directors of Magister Solutions Ltd. He is currently an Editorial Board Member of Wireless Networks and International Journal of Communication Systems.