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arXiv 2012.12114cs.ITcs.SYeess.SYmath.IT

Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network

Yuanzhe Geng, Erwu Liu, Rui Wang, Yiming Liu, Jie Wang, Gang Shen, Zhao Dong

英文摘要:

Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication. However, it is difficult to get an accurate CSI in practical situations. In this letter, we study the outage probability minimizing problem based on optimizing relay selection and transmission power. We propose a prioritized experience replay aided deep deterministic policy gradient learning framework, which can find an optimal solution by dealing with continuous action space, without any prior knowledge of CSI. Simulation results reveal that our approach outperforms reinforcement learning based methods in existing literatures, and improves the communication success rate by about 4%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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