A hybrid multi-agent DRL framework with attention and meta-optimization jointly tunes beamforming, power, RIS configuration, and positions to achieve higher energy efficiency in aerial MF-RIS and fluid-antenna full-duplex networks than benchmarks.
A multiagent deep reinforcement learning approach for multi-UA V cooperative search in mult ilayered aerial computing networks,
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Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
A hybrid multi-agent DRL framework with attention and meta-optimization jointly tunes beamforming, power, RIS configuration, and positions to achieve higher energy efficiency in aerial MF-RIS and fluid-antenna full-duplex networks than benchmarks.