论文标题
合作通信网络中继电器选择和权力分配的深层确定性政策梯度
Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network
论文作者
论文摘要
在考虑合作沟通中的继电器选择和电力分配时，通常需要完美的渠道状态信息（CSI）。但是，在实际情况下很难获得准确的CSI。在这封信中，我们研究了基于优化继电器选择和传输功率的故障概率最小化问题。我们提出了优先的经验重播，有助于确定性政策梯度学习框架，该框架可以通过处理连续的动作空间来找到最佳的解决方案，而无需任何事先了解CSI。仿真结果表明，我们的方法在现有文献中的表现优于强化学习方法，并将沟通成功率提高了约4％。
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%.
