Zan Li

Sparse array synthesis is always a tough issue because of its nonlinear, non-convex characteristic, especially the synthesis with geometry-position constraints is particularly challenging where current methods struggle to effectively handle it. To address these issues, a novel alternating direction method of multipliers (ADMM) with relaxation-projection (RP) is proposed for sparse array synthesis in this work. By decomposing the original complex optimization into two computationally tractable subproblems of auxiliary variable updating and element position updating, the ADMM-RP method significantly improves processing efficiency through specifically designed approaches for each subproblem. Moreover, to address the computationally challenging subproblems, a novel relaxation-projection mechanism is designed, effectively eliminating the infeasible solutions in advance. Besides, geometric constraints are temporarily relaxed in the relaxation stage to obtain an initial feasible solution, significantly reducing computational complexity, whereas element position updating criteria is specifically designed in the projection stage to ensure that all geometric constraints are strictly satisfied. Numerical results for rectangular, symmetric, hybrid, and large-scale arrays under tough constraints demonstrate the superior performance, high computational efficiency, and robustness of the proposed ADMM-RP method, which is highly capable of handling geometry-position-constrained sparse array synthesis.

We investigate the resource management in a sparse code multiple access (SCMA) enabled downlink LEO satellite communication system with multi-beam capability. It considers the joint optimization of beamwidth, subcarrier, and SCMA codebook of the system aiming to minimize the average transmission delay while maximizing the coverage probability under dynamic user distributions. To tackle the exponentially large discrete action space, we propose a MAP-Elites enhanced Multi-Objective Reinforcement Learning (ME-MORL) framework that combines discrete-action Proximal Policy Optimization (PPO) with an evolutionary archive to maintain diverse resource allocation policies while avoiding premature convergence. Extensive experiments demonstrate that in unseen scenarios, it attains the target coverage with only 50% of the training iterations required by competing approaches while maintaining a 24% lower transmission delay, demonstrating superior sample efficiency and cross-scenario generalization.

Unmanned aerial vehicles (UAVs) play a crucial role in modern communication systems owing to their high mobility and broad coverage. However, due to the inherent open nature of the wireless channels, UAV-to-ground links are facing significant security threats from eavesdroppers and malicious jammers. To address these challenges, we propose a dynamic spectrum control (DSC) scheme integrating joint UAV trajectory and transmit power optimization to enhance UAV communication security in this paper. This scheme divides transmission channels from time and frequency dimensions and intelligently generates secure decision sequences using cryptographic principles based on real-time channel states, enabling transmissions for legitimate users without intra-cell interference. Based on a rapid-flooding time synchronization protocol, we analyze inter-cell collision probability (CP) and formulate an optimization problem for the secrecy rate. To further enhance security, we conduct a joint UAV trajectory and transmit power optimization. Through the successive convex approximation (SCA) method, we transform the non-convex optimization problem into a tractable convex form, obtaining a suboptimal solution. Simulations demonstrate that our proposed scheme significantly enhances security compared to conventional UAV communication methods.

This letter investigates multi-user covert communications. Specifically, we propose an intelligent spectrum control (ISC) scheme that employs spectrum sensing-assisted dynamic control to generate time-frequency resource occupation matrices for multiple legitimate users. Guided by these matrices, the users can occupy different frequency slots irregularly for covert data transmission. By performing spectrum sensing in advance, the proposed scheme can also proactively avoid external interference and co-channel collisions among users. In addition, we consider an eavesdropper with wideband detection capability and derive the closed-form expressions for the minimum detection error probability (DEP) of the eavesdropper and the reliable transmission probability (RTP) of the legitimate users. We then optimize the transmit power to maximize the covert rate (CR), and further characterize the maximum number of legitimate users that can access the network covertly and concurrently. Simulation results demonstrate the superiority of the proposed ISC scheme in terms of covertness, reliability, and user capacity.

Path loss (PL) map prediction is an important task for wireless propagation modeling and network planning. Neural network (NN)-based approaches have been widely studied due to their higher efficiency than ray tracing (RT). However, most existing methods focus on two-dimensional (2D) PL maps. To address the three-dimensional (3D) PL map prediction problem, this letter proposes a multi-altitude PL map prediction framework based on an NN architecture. The proposed model adopts a dual-encoder design to extract heterogeneous features from the environment and the transmitter (Tx) location, and uses a height-aware feature fusion module to embed altitude conditioning into the fused features, enabling the network to differentiate PL patterns across altitude planes. Experimental results show that the proposed model improves prediction accuracy over representative baselines, and substantially reduces inference time compared with RT.