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 channel information into a constructed
intelligent agent, and outputtinga corresponding analog
precoding matrix; and calculating a digital
precoding matrix containing the digital precoding vector of each user through zero-forcing precoding and a water injection
algorithm.According to the method, the problems of high
hybrid precoding design complexity and poor reachable rate performance in a large-scale
MIMO system can be effectively solved, and the method has relatively high robustness to a channel environment.