MU-MISO hybrid precoding design method based on multi-agent deep reinforcement learning

Abstract

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.

Description

technical field [0001] The invention relates to a MU-MISO hybrid precoding design method, and is especially suitable for the MU-MISO hybrid precoding design method based on multi-agent deep reinforcement learning used in downlink systems in communication. Background technique [0002] Massive Multiple-Input Multiple-Output (MIMO), as an effective method to improve network transmission rate and energy efficiency, is regarded as one of the key technologies of the new generation wireless communication network. The MIMO system can make full use of space resources and double the system capacity without increasing spectrum resources and antenna transmission power. [0003] However, massive MIMO systems still face many challenges in practical applications. The design of beamforming matrices in mmWave systems is constrained by expensive RF hardware. The traditional all-digital beamforming structure needs to be equipped with a radio frequency link for each transmitting antenna and ...

AI Technical Summary

Problems solved by technology

[0005] Technical problem: Aiming at the deficiencies of the above technologies, provide a solution to the problem of high complexity of hybrid precoding design and poor attainable rate performance in massive MIMO systems, and has strong robustness to the channel environment. MU-MISO hybrid precoding design method for multi-agent deep reinforcement learning

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