Intelligent reflection surface phase optimization method based on deep reinforcement learning

Abstract

The invention discloses an intelligent reflection surface phase optimization method based on deep reinforcement learning. The method comprises the following steps: initializing an action network, an evaluation network, an intelligent reflection surface phase offset matrix and an experience pool in an intelligent reflection surface (intelligent agent); acquiring an initial state of the agent according to the user channel state information; storing the experience pool through interaction between the intelligent reflection surface and the wireless communication system; randomly sampling from theexperience pool to train the action network and the evaluation network so as to maximize the evaluation value output by the evaluation network, and then obtaining network model parameters after convergence; and outputting the optimal phase offset matrix coefficient of the intelligent reflecting surface, which maximizes the receiving signal-to-noise ratio of the user under the channel state information. According to the method, the time required for optimizing the phase offset matrix can be effectively reduced, the storage space of the sample is trained, and the robustness is better.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a phase optimization method for an intelligent reflective surface based on deep reinforcement learning. Background technique [0002] In recent years, the birth of a number of key 5G technologies has significantly improved the spectral efficiency and capacity of wireless communication systems. However, in the actual deployment process, there are still practical problems such as high energy consumption, hardware implementation complexity, and signal processing algorithm complexity. With the development of radio frequency micro-electromechanical systems and metamaterials, the application of intelligent reflecting surfaces (Intelligent Reflecting Surface, IRS) with low energy consumption and adaptable to time-varying wireless communication systems becomes possible. IRS generally consists of a large number of passive printed dipole antenna elements, and each passive antenna c...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the high computational complexity of the SDR algorithm and the sample acquisition problem of deep learning. The present invention provides an intelligent reflective surface optimization method based on deep reinforcement learning for the downlink transmission system of the base station using a large-scale uniform linear antenna array , the proposed algorithm can train the network model online according to the samples in the experience pool, saving sample storage space and phase optimization time

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