Deep learning signal detection method based on conjugate gradient descent method

Abstract

The invention provides a deep learning signal detection method based on a conjugate gradient descent method. The deep learning signal detection method mainly faces a large-scale MIMO system. The method comprises the following steps: (1) constructing a model-driven deep learning network LcgNet based on a conjugate gradient descent method, converting a stepping scalar of each iteration into a network parameter needing to be learned, and improving the dimension of the parameter; (2) modeling a channel environment, and generating a large amount of training data with different signal-to-noise ratios according to an MIMO system model; (3) carrying out offline training on the network by using a large amount of training data; and (4) carrying out online real-time signal detection according to thereceived signal and assumed perfectly known channel state information. By means of the strength of deep learning, the signal detection precision can be improved, and the calculation complexity is further reduced. Besides, the deep learning network is easy to train due to the limited number of required optimization parameters, and has low requirements for time and hardware in the training stage.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a deep learning signal detection method based on a conjugate gradient descent method. Background technique [0002] With the rise of the Internet of Things and the increasing variety of mobile Internet services, people have put forward higher requirements for the data transmission rate and service quality of cellular mobile communications. Because it can fully tap the degree of freedom of the spatial dimension, and obtain better power utilization while improving spectral efficiency, massive MIMO systems have attracted widespread attention at home and abroad. The large-scale antenna array configured by the massive MIMO system not only brings performance gain, but also brings a sharp increase in system hardware complexity and computational complexity. Therefore, a detector with low complexity and good bit error rate performance is very important for the design of MIMO receiver....

AI Technical Summary

Problems solved by technology

However, when the MIMO scale is large, the large matrix inversion operation contained in the linear detector still consumes large computing resources

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