基于多智能体深度强化学习的MU-MISO混合预编码设计方法

本发明公开了一种基于多智能体深度强化学习的MU-MISO混合预编码设计方法，适用于通信中下行系统使用。该方法中基站构建多个用于计算模拟预编码矩阵的深度强化学习智能体，每个智能体包含一个动作预测网络及一个带优先级的经验池，各智能体共用一个中心化的奖励值预测网络和一个中心化的评价网络，协同探索模拟预编码策略。该方法中基站获取多个用户的信道状态信息，将用户信道信息输入所构建的智能体，输出相应的模拟预编码矩阵；进而通过迫零预编码和注水算法计算包含各用户数字预编码向量的数字预编码矩阵。其能有效解决大规模MIMO系统中混合预编码设计复杂度高且可达速率性能不佳的问题，且对信道环境具有较强的鲁棒性。 The invention discloses an MU-MISO hybrid precoding design method based on multi-agent deep reinforcement learning, and the method is suitable for a downlink system in communication. According to themethod, a base station constructs a plurality of deep reinforcement learning agents used for calculating an analog precoding matrix, each agent comprises an action prediction network and an experiencepool with priority, and the agents share a centralized reward value prediction network and a centralized evaluation network to cooperatively explore an analog precoding strategy. The method comprisesthe following steps: enabling a base station to acquire channel state information of a plurality of users, inputting the user chan