Liangshun Wu

Liangshun Wu, Jianbo Du, Junsuo Qu

Efficient computation offloading in multi-UAV edge networks becomes particularly challenging in dense urban areas, where line-of-sight (LoS) links are frequently blocked and user demand varies rapidly. Reconfigurable intelligent surfaces (RISs) can mitigate blockage by creating controllable reflected links, but realizing their potential requires tightly coupled decisions on UAV trajectories, offloading schedules, and RIS phase configurations. This joint optimization is hard to solve in practice because multiple UAVs must coordinate under limited information exchange, and purely model-free multi-agent reinforcement learning (MARL) often learns too slowly in highly dynamic environments. To address these challenges, we propose a decentralized model-based MARL framework. Each UAV optimizes mobility and offloading using observations from several hop neighbors, and submits an RIS phase proposal that is aggregated by a lightweight RIS controller. To boost sample efficiency and stability, agents learn local dynamics models and perform short horizon branched rollouts for proximal policy optimization (PPO) updates. Simulations show near centralized performance with improved throughput and energy efficiency at scale.

Liangshun Wu, Wen Chen

Wideband multi-user transmission assisted by reconfigurable holographic surfaces (RHSs) is fundamentally limited by mutual coupling effect among densely packed sub-wavelength radiation elements. This paper develops a coupling-aware wideband RHS model and an efficient joint beamforming framework to maximize the multi-user sum rate under practical feeder power and RHS excitation power constraints. We establish an electromagnetic equivalent model based on magnetic-dipole elements and a physically interpretable coupling decomposition into free space near field coupling and guided surface wave coupling. For optimization, we employ a weighted minimum mean square error (WMMSE)-based block coordinate method with a closed-form digital precoder update and introduce a Jacobian-aided coupling consistent hologram update that preserves coupling sensitivity via a first-order surrogate while keeping the hologram subproblem convex and efficiently solvable by projected first-order methods. Meep experiments verify the correctness of the proposed coupling model, and the simulations for a 28~GHz, 1~GHz-bandwidth RHS downlink prove the effectiveness of Jacobian-aided WMMSE-based method.