This paper addresses the problem of resource allocation in aerial intelligent reflecting surface (AIRS)-assisted wireless networks, where intelligent reflecting surfaces (IRS) are deployed on flying platforms. Specifically, we investigate the joint optimization of AIRS placement and phase shifts, along with base station subcarrier and power allocation to maximize the system sum rate. To address the problem's non-convexity, an alternating optimization framework is proposed. In this framework, we employ K-means clustering to determine optimal AIRS locations and decompose the problem into two interdependent subproblems: AIRS phase optimization on the one hand and base station subcarrier and power allocation on the other hand, each solved iteratively until convergence is reached. In addition, the successive convex approximation method is used to tackle the non-convexity of AIRS phase shift optimization. Numerical results demonstrate that the proposed approach outperforms benchmark schemes, highlighting the potential of AIRS to extend cellular coverage in challenging scenarios such as emergencies or regions with limited infrastructure.

In this paper, we address optimum design of uncrewed aerial vehicle (UAV)–based wireless networks with a focus on computation offloading in the presence of an active aerial attacker. Our design aims to minimize the maximum computation time among the tasks of ground users while satisfying the energy consumption requirements. To this end, we propose a joint optimization problem of partial computation offloading, ground user association, multiple UAVs trajectory control, computation resource, and sub-channel assignment. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach to iteratively solve the five sub-problems, namely, user-UAV association, user scheduling, partial offloading control and bit allocation over time slots, computation resource and sub-channel assignment, and UAV trajectory control until convergence. Moreover, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the system against jammer attacks. Additionally, we propose low-complexity algorithms to solve the involved sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the impact of an aerial attacker.

In this paper, we study unmanned aerial vehicle (UAV) trajectory control and channel assignment for UAV-based wireless networks with wireless backhauling. Our design aims to maximize the sum rate achieved by ground users while satisfying their data demand where spectrum reuse and co-channel interference management are considered. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach where we iteratively optimize the channel assignment and UAV trajectory control until convergence. Particularly, we propose an efficient heuristic algorithm to solve the channel assignment sub-problem. Moreover, the successive convex approximation (SCA) is used to solve the non-convex UAV trajectory control sub-problem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different network settings.

In this paper, we study the computation offloading problem against an active attacker in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, computation and resource allocation is performed. Our design aims to minimize the maximum computation time of individual tasks among ground users while satisfying energy consumption constraints. To tackle the underlying non-convex optimization problem, we use the alternating optimization approach to iteratively solve three sub-problems, namely, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and UAV trajectory control, until convergence. Furthermore, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the SAGIN against active attacks. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the effect of an active attacker.

In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines.

This paper focuses on the study of hybrid beamforming (HBF) design, which aims to reduce hardware complexity and energy consumption in massive multiple-input and multiple-output (MIMO) systems that operate in frequency-selective channels. The proposed HBF architecture utilizes fixed phase shifters (FPSs) and switches to perform analog beamforming coefficients in an orthogonal frequency division multiplexing (OFDM) multiuser multiple-input and single-output (MISO) system. Our design aims to maximize the downlink sum rate while satisfying power constraints for each sub carrier by jointly optimizing the digital and analog precoder coefficients. To address the underlying non-convex optimization problem, we employ the alternating optimization approach and fractional programming (FP) technique. Through numerical studies, we demonstrate the effectiveness of our proposed design compared to baselines under different network settings.

In this paper, we study the computation offloading problem in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicles (UAVs) trajectory control, user scheduling, computation and resource allocation is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve five sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource, bandwidth allocation, and multi-UAV trajectory control until convergence. In addition, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under different network settings.

In this article, we study the trajectory control, subchannel assignment, and user association design for unmanned aerial vehicles (UAVs)-based wireless networks. We propose a method to optimize the max-min average rate subject to data demand constraints of ground users (GUs) where spectrum reuse and co-channel interference management are considered. The mathematical model is a mixed-integer nonlinear optimization problem which we solve by using the alternating optimization approach where we iteratively optimize the user association, subchannel assignment, and UAV trajectory control until convergence. For the subchannel assignment subproblem, we propose an iterative subchannel assignment (ISA) algorithm to obtain an efficient solution. Moreover, the successive convex approximation (SCA) is used to convexify and solve the nonconvex UAV trajectory control subproblem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different UAV flight periods and number of subchannels and GUs as compared with a simple heuristic.

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicle (UAV) based wireless networks with wireless backhauls. This design aims to maximize the min rate achieved by ground users (GUs) subject to their data transmission demands. To tackle the underlying mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Toward this end, we propose a heuristic algorithm to obtain a feasible solution for the sub-channel assignment sub-problem. In addition, the successive convex approximation (SCA) method is used to convexify and solve the non-convex UAV trajectory control sub-problem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design for different network settings.

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicles (UAVs) based wireless networks with wireless backhaul links. This design aims to optimize the max-min rate subject to data transmission demands of ground users (GUs). The underlying problem is a mixed integer nonlinear optimization problem because of the complicated relationship between the UAV-GU channel gains and the UAV's location in each time slot of the flight period. To tackle this problem, we employ the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Moreover, the difference of convex functions (DC) optimization method and the arithmetic and geometric means (AM-GM) inequality are employed to convexify and solve the non-convex UAV trajectory sub-problem. Via extensive numerical studies, we illustrate the effective UAV's trajectory considering capacity-limited access and backhaul links and the non-negligible rate gain of the proposed design compared to a baseline employing the circular UAV trajectory around the center of service area and a heuristic algorithm for sub-channel assignment.

In this paper, we study the problem of unmanned aerial vehicles (UAVs) placement and bandwidth allocation for wireless networks with wireless backhaul links. The general model with different possible configurations of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions of wireless links between UAVs and ground users (GUs) is explicitly considered based on which we derive the average rates for wireless access links. The underlying problem is a difficult non-convex optimization problem due to the strong co-channel interference and the complicated relationship between the LoS/NLoS probabilities and UAVs' locations. To solve this challenging problem, we employ the alternative optimization approach where we iteratively optimize the bandwidth allocation and UAV placement until convergence. Moreover, we employ the difference of convex functions (DC) optimization and quadratic transformation approaches to convexify and tackle the non-convex UAV placement sub-problem. Via numerical studies, we show that the proposed scheme achieves a significant throughput gain compared to a baseline in which UAVs are deployed at the centers of hotspot areas.