A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications
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JSACC dynamically switches between secrecy and covert modes using RIS, derives closed-form outage probability and ergodic rate, shows diversity order depends on Nakagami parameters and RIS elements, and outperforms conventional secrecy communication in simulations.
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Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications
A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
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Joint Secrecy and Covert Communication (JSACC): An Enhanced Physical Layer Security Approach
JSACC dynamically switches between secrecy and covert modes using RIS, derives closed-form outage probability and ergodic rate, shows diversity order depends on Nakagami parameters and RIS elements, and outperforms conventional secrecy communication in simulations.