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This is a Quantitative Study study.

DRL‐Based Secure Communication for Reconfigurable Intelligent Surface‐Assisted MISO Systems

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Simulation shows improved secrecy rate through deep reinforcement learning in RIS technology, indicating efficiency in complex environments.

The proposed method enhances secrecy rates in MISO systems using deep reinforcement learning.
Utilizing the secrecy rate as an immediate reward, the method optimizes RIS beam-forming effectively.
Employing deep deterministic policy gradient, the scheme overcomes challenges in channel environments for RIS.
Simulation results reveal significant improvements in achieving maximum secrecy rates in real-time conditions.

Haowen et al. (2025) studied this question.

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View Full Paper

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Authors

Simulation shows improved secrecy rate through deep reinforcement learning in RIS technology, indicating efficiency in complex environments.

The proposed method enhances secrecy rates in MISO systems using deep reinforcement learning.
Utilizing the secrecy rate as an immediate reward, the method optimizes RIS beam-forming effectively.
Employing deep deterministic policy gradient, the scheme overcomes challenges in channel environments for RIS.
Simulation results reveal significant improvements in achieving maximum secrecy rates in real-time conditions.

Haowen et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark