This study investigates the joint optimization of trajectory and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless-powered two-way communication (WPTWC) under probabilistic line-of-sight (LoS) channel models. In this communication protocol, the UAV transmits signals over wireless links while the ground nodes (GNs) simultaneously receive information and harvest energy based on either a power splitting (PS) or time switching (TS) policy. Each GN then uses the harvested energy to transmit collected data back to the UAV. Due to the nature of downlink broadcast transmission in UAV-enabled WPTWC, where GNs at different locations receive signals simultaneously, both LoS and non-LoS components significantly influence the channel characteristics. To capture these location-dependent effects accurately, we reformulate both components into equivalent convex forms, which enhances analytical tractability. Subsequently, we jointly optimize the time allocation, the three-dimensional UAV trajectory, the transmit powers of the UAV and GNs, and the energy harvesting ratio of the GNs to maximize the minimum uplink spectral efficiency (SE) of the GNs while satisfying the downlink SE requirements for all GNs. To tackle the nonconvexity of the optimization problem, we first decompose it into four subproblems and then transform each subproblem into a convex problem with respect to its corresponding optimization variable via successive convex approximation and advanced optimization methods. Thereafter, we propose a low-complexity algorithm that employs the block coordinate descent method to iteratively find the optimal solution for each convex subproblem. Simulations reveal distinct trajectory and resource allocation behaviors under the PS and TS policies, influenced by the downlink SE requirement. Furthermore, by adaptively optimizing the UAV trajectory and radio resource allocation based on the network conditions, the proposed scheme significantly improves the minimum uplink SE of the GNs relative to baseline schemes.

This study explores interference coordination for space-air-ground integrated networks under probabilistic line-of-sight (LoS) channel models, where the LoS probability of a wireless channel is statistically modeled based on the elevation angle between an unmanned aerial vehicle (UAV) and a ground node (GN). Unlike conventional approaches that simplify the model by neglecting either LoS or non-LoS (NLoS) channel components, we jointly optimize user scheduling, transmit power, and three-dimensional trajectory while fully accounting for both components. The objective is to maximize the minimum average spectral efficiency (SE) among GNs while ensuring the required SE for earth stations (ESs) served by satellites. Given the nonconvexity of the optimization problem, we decompose it into four subproblems and apply a successive convex approximation to make each subproblem convex with respect to the relevant optimization variable. Subsequently, we propose a low-complexity algorithm based on the block coordinate descent method to iteratively determine the optimal solution for each convex subproblem. Extensive simulations under various scenarios confirm that the UAV optimizes its horizontal and vertical trajectories to increase the LoS probability of the signal channel and the NLoS probability of the interference channel. This allows the UAV to serve GNs with high SE while reducing interference to the ESs. The results also demonstrate that the proposed scheme outperforms baseline schemes in terms of average SE by jointly optimizing the three-dimensional trajectory and communication resources.

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.

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.

In this study, we explore a three-dimensional trajectory and pick-up design of an unmanned aerial vehicle (UAV) for parcel delivery. In particular, we consider the real-world scenario in which a weight-restricted UAV cannot pick up all parcels within a single route; therefore the parcel delivery must be divided into multiple trajectories while avoiding no-fly zones. We formulate this problem mathematically as the minimization of total delivery time, which jointly optimizes the pick-up indicators, the lengths of the time slots, and the horizontal and vertical trajectories. To address the non-convexity of the formulated mixed-integer nonlinear programming, we employ a successive convex approximation to convert the problem into a convex form concerning optimization variables and utilize a penalty convex-concave procedure to preserve the binary characteristics of the pick-up indicators. Subsequently, we propose an iterative algorithm based on a block decent algorithm to efficiently identify the optimal solution by solving the relaxed convex problem. To address the problem of high computational complexity associated with the optimization-based algorithm, we also present an unsupervised deep learning (DL)-based heuristic algorithm. The simulation results confirm that the proposed schemes achieve considerably shorter delivery times than the baseline schemes in various scenarios. Furthermore, the DL-based scheme requires about 10% longer delivery time than the optimization-based scheme, but it can approximate the UAV strategy with substantially reduced computation time.