强化学习
继电器
计算机科学
反射(计算机编程)
选择(遗传算法)
吞吐量
相(物质)
选择算法
数学优化
人工智能
无线
电信
数学
功率(物理)
物理
量子力学
程序设计语言
作者
Chong Huang,Gaojie Chen,Yu Gong,Miaowen Wen,Jonathon A. Chambers
出处
期刊:IEEE Wireless Communications Letters
[Institute of Electrical and Electronics Engineers]
日期:2021-05-01
卷期号:10 (5): 1036-1040
被引量:40
标识
DOI:10.1109/lwc.2021.3056620
摘要
This letter proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods.
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