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%.

翻译：在考虑合作通信中的继电器选择和权力分配时,通常需要完美的频道状态信息。然而,很难在实际情况下获得准确的 CSI。在本信中,我们研究了根据优化中继选择和传输能力而最大限度地减少问题的断电概率。我们提出了一个优先经验重播的深层确定性政策梯度学习框架,该框架可以通过在不事先了解CSI的情况下处理连续行动空间找到最佳解决办法。模拟结果表明,我们的方法优于现有文献中的强化学习方法,并使通信成功率提高约4%。