Intelligent reflecting surface regulation and control method and device based on deep reinforcement learning

Abstract

The invention provides an intelligent reflecting surface regulation and control method and device based on deep reinforcement learning. The method comprises the steps of: enabling a strategy network to generate a first action according to a first state; fixing and inputting the amplitude into an optimization module, updating the first action to obtain a second action, and meanwhile obtaining a first target value; acting the second action on the wireless environment to obtain a second state, obtaining a new sample and storing the new sample into an experience pool; enabling the strategy networkand the value network to carry out DDPG training according to the samples, and enabling an executor to update parameters of the executor by using a strategy gradient method; determining a third target value according to the first target value and a second target value generated by the target Q network, training the DNN of the online Q network according to the third target value, and updating parameters of the DNN; and repeating the above steps until the change amplitude of the transmitting power is smaller than a preset threshold value, and obtaining and outputting the network parameter for minimizing the AP transmitting power. According to the method, stable and efficient learning can be realized in a shorter time, and the optimal target can be converged more quickly.

Description

technical field [0001] The invention relates to the technical field of wireless communication networks, in particular to a control method and device for an intelligent reflective surface based on deep reinforcement learning. Background technique [0002] At present, IRS is considered to be a very potential and promising technology. IRS is composed of a large number of passive reflection elements, which are connected to each other and controlled by an embedded IRS controller. It can be used to improve the energy and spectrum of wireless communication. efficiency. Signal strength at the receiver is enhanced through joint control of the complex reflection coefficients of all reflective elements, known as passive beamforming. The passive beamforming technology of the IRS and the transmission control technology of the transceiver can further improve the network performance. IRS has been applied in various scenarios, which play different roles in wireless communication, such as ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a control method and device for intelligent reflective surfaces based on deep reinforcement learning to solve the technical problems of low learning efficiency and poor stability of traditional deep reinforcement learning methods

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