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Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems
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DOI：10.1109/LCOMM.2021.3117929.png)
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摘要
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This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together, for the first time in the literature. The goal is to maximize the rate by optimizing the RIS phase shifts. A novel deep reinforcement learning (DRL) algorithm is proposed to solve the formulated non-convex optimization problem. The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting. Besides, it significantly reduces the computational complexity of the downlink HD MISO system and improves the achievable rate with a reduced number of steps per episode compared to the conventional DRL algorithm.
Keyword:
MISO communication
Optimization
Wireless communication
Downlink
Array signal processing
Reinforcement learning
Computational complexity
Reconfigurable intelligent surface (RIS)
half-duplex full-duplex (HD-FD)
deep reinforcement learning (DRL)
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
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IEEE ACCESS
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