This paper investigates the joint optimization of trajectory, user scheduling, and time-slot duration in unmanned aerial vehicle (UAV)-assisted wireless communication systems under minimum expected spectral efficiency (SE) constraints. Unlike most existing studies that approximate the expected SE by substituting the random channel gain with its mean value, thereby evaluating the SE at the average channel realization and overestimating the true expected SE due to Jensen's inequality, we approximate the expected SE by numerically integrating the SE over the channel distributions. Specifically, instead of relying on average-channel-based approximations, we develop a conservative yet tractable quadrature-based approximation by discretizing the associated cumulative distribution functions. The resulting finite-sum representation explicitly accounts for the probabilistic LoS structure and channel fading effects, while remaining tractable for optimization. Leveraging this lower bound, we formulate a mission completion time minimization problem subject to minimum expected-SE requirements for all ground nodes. The resulting problem is a mixed-integer nonconvex optimization, which is tackled via a penalty-based block coordinate descent framework. The proposed algorithm alternately optimizes the scheduling decisions and the UAV trajectory along with adaptive time-slot durations, and maintains feasibility with respect to the original expected-SE constraints by leveraging successive convex approximation and quadratic transform techniques. Simulation results demonstrate that the proposed method strictly satisfies the minimum expected-SE constraints and achieves a significantly shorter mission completion time than conventional average-channel-based approaches, which are shown to yield infeasible or overly conservative solutions.
Recent advances in unmanned aerial vehicle (UAV) platforms have significantly expanded their role in wireless communications, driven by rapid improvements in hardware capability and substantial reductions in deployment cost [1], [2]. These developments have positioned UAVs as a promising architectural component for future wireless networks. Unlike conventional terrestrial base stations, UAVs possess inherent mobility in three-dimensional (3D) space, enabling flexible positioning that can be exploited to adapt network topology according to communication demands. By operating at elevated altitudes, UAVs can mitigate the impact of obstacles such as buildings and terrain, resulting in more favorable airto-ground channel characteristics and improved link reliability
The authors are with the Department of Information and Communication Engineering, Dongguk University, Seoul 04620, South Korea (e-mail: kslee851105@gmail.com). [3]. This mobility advantage is particularly beneficial in scenarios where terrestrial infrastructure is unavailable, damaged, or economically infeasible. In such environments, UAVs can act as aerial data collectors for ground nodes (GNs) distributed across remote or hard-to-access regions.
The inherent mobility of UAVs has prompted extensive research on the joint optimization of UAV trajectories and communication resources to improve wireless network performance [4]- [16]. Existing studies primarily focused on different performance objectives, including coverage enhancement [4], [5], spectral efficiency (SE) maximization [6], [7], and reliability improvement under adverse channel conditions [8], [9]. Along this line of research, joint optimization of user scheduling, transmit power, and UAV trajectory was investigated to mitigate co-channel interference and improve system throughput [10], [11]. Cooperative UAV architectures were also explored, where base-station UAVs coordinated with jammer UAVs to enhance secure communications [12], [13]. More recently, UAV-assisted communication systems with GNs equipped with energy-harvesting capabilities attracted growing attention, in which UAV trajectories and communication resources were jointly optimized to support wireless power transfer and data transmission [14]- [16]. A common underlying assumption in most of the aforementioned studies is the dominance of line-of-sight (LoS) air-to-ground links, under which simplified channel models were often adopted to maintain tractable optimization. However, such assumptions are difficult to justify in practical deployment scenarios, where signal blockage caused by environmental obstacles is unavoidable. This issue becomes particularly pronounced when UAVs operate at relatively low altitudes, as reduced elevation angles significantly increase the likelihood of non-line-of-sight (NLoS) propagation [17].
To address these limitations, probabilistic LoS channel models were proposed to characterize the mixed LoS/NLoS nature of air-to-ground communications by explicitly modeling the dependence of LoS probability on UAV altitude [17]. Using such models, recent studies have reexamined UAV trajectory and resource allocation problems under more realistic channel conditions [18]- [28]. In particular, some studies focused on UAV trajectory design under energy-related constraints or objectives, jointly accounting for propulsion energy consumption and communication performance [18]- [20]. Others investigated time-constrained UAV operations, where trajectories and radio resources are optimized to satisfy latency or mission completion requirements [21], [22]. In [23], UAV-enabled data harvesting was studied using adaptive offline-online trajectory design under time-varying channel conditions, while [24] considered trajectory optimization to enhance robustness against jamming attacks. More recent studies extended these designs to multi-UAV systems, where coordination among multiple UAVs is exploited to improve fairness, interference management, or overall system performance [25], [26]. Probabilistic LoS channel models were also adopted in UAV-enabled wireless-powered networks, where the UAV primarily serves as an energy transmitter and trajectory design is coupled with power transfer strategies to support information delivery [27], [28].
Despite these advances under probabilistic LoS channel models, the expected SE in many existing studies [18]- [28] is still approximated by substituting the instantaneous channel with its mean value to simplify the optimization problem. As a result, such average-channel-based approximations systematically overestimate the true expected SE due to Jensen’s inequality. While this practice is computationally convenient, its implications for system reliability have largely been overlooked, particularly in joint UAV trajectory and resource optimization, where inaccuracies in expected-SE characterization can distort the resulting UAV strategy and compromise reliability. These observations motivat
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