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.
3D deployment and energy efficiency optimization based on DRL for RIS-assisted air-to-ground c ommunica- tions networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.