The low earth orbit (LEO) satellite communication network has recently been proposed by 3GPP as a new paradigm of infrastructure to enhance the capacity and coverage of existing terrestrial wireless networks. However, the mobility of LEO satellite nodes leads to a dynamic environment, which introduces unique challenges for handover and throughput optimization in multi-user access control for LEO networks. We formulate an optimization problem of joint access control and resource allocation to maximize the long-term system throughput and avoid frequent handovers, which is non-deterministic polynomial-time hard. To overcome this challenge problem, we propose a multi-agent deep reinforcement learning algorithm and design the proximal policy optimization (PPO) network structure with the long short-term memory (LSTM) layers. In our proposed algorithm, the centralized trainer node is responsible for training the parameters of all networks, and then each ground user independently makes its own access decisions based on its local observation. We deploy a policy network on each ground user that is able to intelligently access a proper LEO satellite node to maintain high system throughput and avoid frequent handovers over a long period. The simulation results have demonstrated the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in addressing the access control and resource allocation issue for the multi-user LEO satellite network.

This paper proposes a novel towed movable antenna (ToMA) array architecture to enhance the physical layer security of airborne communication systems. Unlike conventional onboard arrays with fixed-position antennas (FPAs), the ToMA array employs multiple subarrays mounted on flexible cables and towed by distributed drones, enabling agile deployment in three-dimensional (3D) space surrounding the central aircraft. This design significantly enlarges the effective array aperture and allows dynamic geometry reconfiguration, offering superior spatial resolution and beamforming flexibility. We consider a secure transmission scenario where an airborne transmitter communicates with multiple legitimate users in the presence of potential eavesdroppers. To ensure security, zero-forcing beamforming is employed to nullify signal leakage toward eavesdroppers. Based on the statistical distributions of locations of users and eavesdroppers, the antenna position vector (APV) of the ToMA array is optimized to maximize the users’ ergodic achievable rate. Analytical results for the case of a single user and a single eavesdropper reveal the optimal APV structure that minimizes their channel correlation. For the general multiuser scenario, we develop a low-complexity alternating optimization algorithm by leveraging Riemannian manifold optimization. Simulation results confirm that the proposed ToMA array achieves significant performance gains over conventional onboard FPA arrays, especially in scenarios where eavesdroppers are closely located to users under line-of-sight (LoS)-dominant channels.

Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system to fully exploit its maximum spatial diversity for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.

In this paper, we propose to employ a movable-antenna (MA) array to enhance unmanned aerial vehicle (UAV) covert communications by fully exploiting the spatial degrees of freedom (DoFs) in large-scale adjustment of UAVs’ positions within broad areas and small-scale movement of MAs within local regions. Specifically, to guarantee fairness, we formulate an optimization problem to maximize the minimum achievable rate over all users via UAV trajectory, transmit beamforming, and antenna position design, subject to a covertness constraint. To solve this non-convex optimization problem, we develop a two-step method to obtain a sub-optimal solution. Specifically, we first design the UAV trajectory under the assumption of ideal beam patterns, which significantly decouples the UAV trajectory optimization and directional transmit beamforming. Then, an alternating optimization algorithm with the successive convex approximation (SCA) technique is developed to optimize the UAV transmit beamforming and MAs’ positions. Simulation results demonstrate that our proposed system design can effectively enhance spectrum-efficiency and stealth of UAV downlink transmissions, significantly outperform conventional systems with fixed-position antenna (FPA) arrays, and closely approach the performance upper bound with ideal beam patterns.

In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and can attain considerable performance improvement compared to the conventional fixed antenna (FA)-based schemes.

Unmanned aerial vehicles (UAVs)-assisted wireless communication will play an important role in the next-generation mobile communication network. However, the inherent open nature of the signal propagation environment may cause illegal eavesdropping and surveillance from adversaries. In addition, the intergroup co-channel interference among different cells further degrades the system performance. Hence, we consider a generic scenario of multiple UAV base stations (UAV-BSs) and ground users, where multiple terrestrial wardens attempt to detect the transmissions from UAV-BSs to users and a UAV-mounted jammer is employed to generate artificial noise to assist the covert communications. To ensure fairness, we formulate an optimization problem to maximize the minimum of the average rate lower bounds of all users by jointly optimizing user association, bandwidth allocation, UAV transmit power control, and UAV 3-D deployment, subject to the constraints of the detection error probability of each warden. To solve this mixed-integer nonconvex problem, we propose a suboptimal algorithm by applying block coordinate descent (BCD) method to solve three subproblems iteratively. Specifically, in each iteration, the subproblem of user association and bandwidth allocation is solved by a customized genetic algorithm (GA) first, where a closed-form expression for bandwidth allocation is obtained. Second, the subproblem of UAV transmit power control is solved by using successive convex approximation (SCA) techniques. Finally, suboptimal 3-D positions of the UAVs are obtained through particle swarm optimization (PSO)-based algorithm. Extensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in terms of improving the minimum of the average rate lower bounds of all users.

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.