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 5%.

中文翻译：

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协作通信网络中中继选择和功率分配的深度确定性策略梯度


在考虑协作通信中的中继选择和功率分配时，通常需要完美的信道状态信息（CSI）。然而，在实际情况中很难获得准确的CSI。在这封信中，我们研究了基于优化中继选择和传输功率的中断概率最小化问题。我们提出了一种优先经验回放辅助的深度确定性策略梯度学习框架，该框架可以通过处理连续动作空间找到最优解决方案，而无需任何 CSI 的先验知识。仿真结果表明，我们的方法优于现有文献中基于强化学习的方法，并将通信成功率提高了约 5%。