This study challenges the conventional view that wireless signal blockages are solely detrimental by exploring how non-line-of-sight (NLoS) channels can reduce co-channel interference and improve network performance. By leveraging the mobility of unmanned aerial vehicles (UAVs), this research examines the joint optimization of communication resources and UAV trajectories in multi-UAV-enabled wireless networks to maximize the minimum spectral efficiency (SE) among ground nodes (GNs) by coordinating co-channel interference. We first propose a new analytical model to identify potential signal blockages caused by multiple buildings and a novel building avoidance method that ensures safe and efficient UAV operations. To solve the problem formulated as non-convex mixed-integer nonlinear programming, we employ various optimization techniques: We first decompose the original problem into multiple convex subproblems for each optimization variable using quadratic transform and successive convex approximation. The penalty convex-concave procedure is applied to maintain the binary nature of scheduling indicators. To efficiently address signal blockage and building avoidance constraints, the separating hyperplane theorem is applied along with the approximation of the indicator function. Finally, we utilize block coordinate descent algorithm to iteratively solve the convex subproblems in sequence. The simulation results confirm that UAVs optimize their trajectories to establish LoS channels for transmitting desired signals to scheduled GNs while forming NLoS channels to mitigate interference for others. In this way, the network performance compared to baseline schemes is significantly enhanced. Furthermore, under the proposed building avoidance constraint, UAVs maintain continuous trajectories without violating building boundaries.

This study re-examines non-line-of-sight (NLoS) channels, proposing a novel approach that leverages three-dimensional (3D) building blockage to mitigate interference signals, thereby enhancing space-air-ground integrated network performance. Unmanned aerial vehicles (UAVs) benefit from mobility, allowing the adaptable formation of line-of-sight (LoS) and NLoS channels by considering building blockage. Accordingly, a mathematical model is presented to determine whether the interference channels from the UAV to satellite nodes (SNs) are blocked by buildings. We then formulate a joint optimization problem involving scheduling, transmit power, and trajectory to maximize the minimum throughput of ground nodes (GNs), ensuring the minimum required throughput for the SNs. We employ various optimization techniques to solve the formulated nonconvex problem and find that this approach requires significant computational complexity and its performance is sensitive to initialization. To address these challenges, we propose an integrated approach, combining an unsupervised learning-based deep learning (DL) framework for determining initial values with subsequent refinement through optimization. Simulation results provide useful insights into employing building blockage for interference mitigation. Notably, the UAV avoids direct access to GNs in areas that can form the LoS interference channels to the SNs and stays in NLoS areas to serve all GNs, preventing severe interference with the SNs. The integrated approach exhibits superior performance with a much faster convergence time compared to the optimization approach by improving the strategy inferred by our DL model with optimization methods.

In multibeam satellites, transmit power is a limited resource shared among beams, and allocating higher power to certain beams may cause more interference with others. Moreover, beam placement is the issue of determining the center position of each beam and potentially leads to interbeam interference depending on the locations of ground nodes. Therefore, in this study, we investigate the joint optimization problem of beam placement and transmit power to maximize the sum spectral efficiency in multibeam low-Earth-orbit satellite communication systems, taking into account the interbeam interference and user distribution. We solve the optimization problem using the gradient ascent method and quadratic transform, and then propose an optimization-based algorithm that iteratively searches for the beam center positions and transmit power levels. To reduce the complexity of this iterative algorithm, we present a deep neural network (DNN) architecture and a training method for approximating optimal solutions, and propose a deep-learning (DL)-based algorithm that quickly infers the optimal values using the pretrained DNN. Simulation results show that the two proposed algorithms have a clear tradeoff in performance between the spectral efficiency and the computation time. In particular, the DL-based algorithm achieves 3% lower spectral efficiency than the optimization-based algorithm; however, the computation time can be significantly reduced. Furthermore, both schemes achieve at least 10% higher spectral efficiency than the benchmark schemes without joint optimization by optimally adjusting both the beam center position and transmit power according to the node distribution and satellite environments.

This study considers an unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) system in which the UAV broadcasts wireless signals to ground nodes (GNs) that receive information and harvest energy simultaneously using a policy of power splitting (PS) or time switching (TS). While taking into account the throughput and fairness of GNs, we investigate a joint optimization of the trajectory, transmit power of the UAV, and the energy harvesting (EH) ratio of the GNs to maximize the sum of the logarithmic average throughput of the GNs while ensuring the average harvested energy requirement for each GN in terms of both linear and nonlinear EH models. We employ a successive convex approximation method to address the nonconvex nature of this problem, which stems from incorporating a nonlinear EH model. This approach allows us to approximate the problem as convex for each control parameter. Thereafter, we propose a respective iterative algorithm based on the block coordinate descent method to identify the optimal solution for each convex problem under each PS or TS policy. Extensive simulations confirm that the proposed method improves average throughput and fairness index while satisfying the EH constraint by effectively optimizing the three control parameters, thereby achieving a near-optimal performance that is superior to existing baseline methods. Our results also reveal significant differences in the UAV trajectory and resource allocation patterns between linear and nonlinear EH models under the PS and TS policies. Furthermore, we explore the practical aspects of EH by comparing both EH models, such as the limitations of the linear EH model in satisfying the EH requirement in real-world SWIPT environments. The findings underscore the importance of considering nonlinear EH models in practical UAV-assisted SWIPT environments.

In this paper, we explore a joint optimization of unmanned aerial vehicle (UAV) trajectory and communication resources with complete avoidance of no-fly-zones (NFZs). In particular, we introduce a new constraint that allows the UAV to perfectly avoid NFZs throughout the entire continuous trajectory with rigorous mathematical proof. Under the proposed constraint on NFZs, we aim to optimize the scheduling, transmit power, length of the time slot, and trajectory of the UAV to maximize the minimum throughput among ground nodes without violating NFZs. To find the optimal UAV strategy from the non-convex optimization problem formulated here, we use various optimization techniques, such as quadratic transform, successive convex approximation, and the block coordinate descent algorithm. Simulation results confirm that the proposed constraint prevents NFZs from being violated over the entire trajectory in any scenario. Furthermore, the proposed scheme shows significantly higher throughput than the baseline scheme using the traditional NFZ constraint by achieving a zero outage probability due to NFZ violations.

In this paper, we investigate the optimal resource allocation in unmanned aerial vehicle (UAV)-assisted wireless-powered two-way communications. The communication process considered here consists of two steps. First, the UAV transmits a control signal over wireless links while ground terminals (GTs) receive information and harvest energy simultaneously, with each GT then using the harvested energy to send data to the UAV. We aim to maximize the minimum uplink throughput among GTs while ensuring the minimum requirement of the downlink throughput for each GT by optimizing the time allocation, the transmit power and the trajectory of the UAV along with the energy harvesting ratio of GTs. First, we propose an effective optimization-based approach to address the non-convexity of the formulated problem, which is difficult to solve. Specifically, we apply a successive convex optimization technique to approximate the convex problem for each optimization variable and find the optimal resource management strategy through a block coordinate descent algorithm. To reduce the high computational complexity of the optimization-based approach, we also develop a deep learning (DL)-based approach consisting of an efficient deep neural network framework and a novel training methodology. Simulation results confirm that the proposed schemes show significant performance improvements over existing baseline schemes. We also confirm that the DL-based scheme achieves performance comparable to the optimization-based scheme with a much shorter computation time.

This paper presents a novel framework that combines an optimization-based approach with a deep learning (DL)-based approach to devise a cooperative strategy for unmanned aerial vehicles (UAVs) in wireless-based secure communications by leveraging the strengths of both approaches and addressing their respective limitations. We first formulate a joint optimization problem to maximize the minimum achievable secrecy rate while guaranteeing the minimum harvested energy requirement for each ground node by optimizing scheduling, transmit power, and trajectory. To address the difficulty of solving the formulated non-convex mixed-integer nonlinear programming problem, optimization techniques, such as continuous convex approximation and the block coordinate descent algorithm, are used to efficiently find feasible solutions. To tackle the challenges posed by the high computational complexity and initialization sensitivity of the optimization-based approach, we also propose an unsupervised learning-based deep neural network (DNN) structure with a specialized loss function tailored to our goals that allows the DNN to effectively approximate the optimal strategies for UAVs. Finally, we design a pioneering method that integrates the strengths of the two aforementioned approaches, in which the output of the trained DNN serves as the initial values of the optimization variables, and subsequently, optimization techniques are applied to fine-tune these optimization variables, leading to further performance improvements. Through intensive simulations, we confirm that the integrated scheme provides superior performance without constraint violation compared to the DL-based scheme, while guaranteeing faster convergence than the optimization-based scheme.