A method of mm-wave sparse front channel estimation based on deep learning network

Abstract

The invention discloses a millimeter-wave sparse front channel estimation method based on a deep learning network. The sparse characteristics of the millimeter-wave channel are used as prior information to train and design a fully connected deep neural network for estimation of the millimeter-wave front communication channel. First, a fully connected phase shifter network is used to design an isotropic analog transceiver by configuring the phases of each phase shifter to be uniformly distributed; then the obtained channel sparse information and the designed optimal digital estimator are used as the fully connected deep learning network. training data. For the sparse channel under each signal-to-noise ratio, the sparse information of the channel is input into the network to obtain the corresponding digital estimator, and then obtain the channel estimation result. The sparse channel estimator provided by the present invention can reduce the error caused by the nonlinear quantization of the low-precision analog-to-digital converter, and implement it using a deep learning network, thereby reducing the complexity of channel estimation, and the performance of the present invention can approach the theoretically optimal channel estimation method.

Description

A channel estimation method for millimeter wave sparse front based on deep learning network technical field The present invention relates to communication field, relate to channel estimation method, more specifically relate to a kind of based on depth science A millimeter-wave sparse-front channel estimation method for learning networks. Background technique In recent years, communication technology has made breakthrough progress and gradually matured, and mobile communication in the global The industry has developed rapidly. Multiple-input multiple-output (MIMO) technology is one of the key technologies in the development of communication technology. The data transfer rate of the system is improved. The transmitter and receiver of the system are equipped with multiple antennas, and the two ends of the transmitter and receiver are used. The multiple antennas form diversity, which can improve system stability. At the same time, due to the number of independent chan...

AI Technical Summary

Problems solved by technology

Channel estimation in traditional large-scale multi-antenna technology systems is a big challenge in itself. The use of low-precision analog-to-digital converters in hybrid architectures makes channel estimation more difficult while reducing cost and power consumption.

At the same time, how to reduce the estimation complexity and pilot overhead while utilizing the sparsity of the mmWave channel, and obtain high-precision channel estimation is also a big challenge

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