Channel estimation method for passive intelligent reflection surface based on deep learning

Abstract

The invention discloses a channel estimation method for a passive intelligent reflection surface based on deep learning. The channel estimation method is realized by an offline channel estimation stage and an online channel prediction stage. In the off-line channel estimation stage, in an uplink, a user side sends a pilot signal, a base station side controls an IRS to sequentially open passive elements to reflect an incident pilot signal, and the base station side receives the pilot signal and estimates corresponding cascade channel information through adoption of a minimum mean square error method. An equal probability uniform sampling method is adopted to select a small amount of sampling cascade channel information from the estimated cascade channel information, and the small amount of sampling cascade channel information and complete cascade channel information are adopted to construct a new data set; and in the online channel prediction stage, the base station side estimates a small amount of sampling cascade channel information online and inputs the sampling cascade channel information to the trained ResNet network to recover complete cascade channel information. According to the invention, the number of passive elements can be flexibly selected and the residual units of the residual neural network can be set so as to meet the service quality characteristics of different systems and users.

Description

Technical field [0001] The present invention relates to the field of wireless communication technology, in particular a channel estimation method based on depth learning passive intelligent reflection surface. Background technique [0002] With the commercialization of 5G communication network, large-scale multi-input multi-output transmissions brings huge energy consumption and hardware complexity. Recently, you have recently been explored the next generation (6G) communication technology, where Intelligent ReflectingSurface, IRS is highly received by its low-cost, low hardware complexity, and characteristics that vulnerable to wireless communication environments. . IRS is a two-dimensional surface having electromagnetic properties, composed of a highly active reconstructed reflective unit made of super material. These units interact with the incident signal by controlling the phase, amplitude, frequency, and the incident signal to improve the coverage and rate of the wireless c...

AI Technical Summary

Problems solved by technology

When IRS assists in wireless communication, the base station adjusts the reflected beam to change the transmission environment. However, due to the high-dimensional cascaded channel and a large number of passive reflective elements, it is estimated that the cascaded channel state information will generate a large amount of pilot training overhead and hardware complexity. Spend

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