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Wanzhe Wang

Publications and source records attributed to Wanzhe Wang.

2 recordsLinked to original sources
arXiv2026

MAGRPO: Accelerated MARL Training for Fluid Antenna-Assisted Wireless Network Optimization

Fluid antenna systems (FASs) improve wireless links by repositioning antenna elements to exploit favorable spatial channel variations. Jointly optimizing fluid antenna (FA) positions, beamforming, and transmit power in a multi-cell network is challenging because the problem is non-convex and each base station has only local information during decentralized execution. However, the representative multi-agent reinforcement learning (MARL) algorithms, namely the on-policy multi-agent proximal policy optimization (MAPPO) and the off-policy multi-agent twin delayed deep deterministic policy gradient (MATD3), suffer from excessively long training times. To address this challenge, we formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose multi-agent group relative policy optimization (MAGRPO) under centralized training with decentralized execution. MAGRPO constructs relative advantages from groups of joint trajectories, thereby eliminating the centralized critic and generalized advantage estimation used by MAPPO; under parameter sharing, this critic-free design reduces the per-step computational complexity by approximately half. Simulations show that joint FA optimization provides several-fold sum-rate gains over fixed-position configurations. Across the evaluated antenna settings, MAGRPO achieves test sum rates higher than those of MATD3 and comparable to or slightly higher than those of MAPPO. Meanwhile, it reduces the training time by 20%-23% compared with MAPPO and by about 35% compared with MATD3.

cs.IT↗
arXiv2025

LLM Agent for Hyper-Parameter Optimization

Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters optimization approaches for Warm-Start Particles Swarm Optimization with Crossover and Mutation (WS-PSO-CM) algorithm, designed for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication, are primarily heuristic-based, exhibiting low levels of automation and improvable performance. In this paper, we design an Large Language Model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and Model Context Protocol (MCP) are applied. In particular, the LLM agent is first set up via a profile, which specifies the boundary of hyper-parameters, task objective, terminal condition, conservative or aggressive strategy of optimizing hyper-parameters, and LLM configurations. Then, the LLM agent iteratively invokes WS-PSO-CM algorithm for exploration. Finally, the LLM agent exits the loop based on the terminal condition and returns an optimized set of hyperparameters. Our experiment results show that the minimal sum-rate achieved by hyper-parameters generated via our LLM agent is significantly higher than those by both human heuristics and random generation methods. This indicates that an LLM agent with PSO and WS-PSO-CM algorithm knowledge is useful in seeking high-performance hyper-parameters.

cs.IT↗