Large-scale MIMO downlink user scheduling method based on deep learning

Abstract

The invention discloses a large-scale MIMO downlink user scheduling method based on deep learning, and the method comprises the steps: obtaining group users, wherein each group comprises the statistical channel information of a plurality of users, and calculating the beam forming index and channel gain of each user; carrying out user scheduling on each group of users by adopting a maximum approximation sum rate method to generate training data; constructing a convolutional neural network model of a prediction system user scheduling scheme and performing offline training, so that each user scheduling probability vector predicted by the model is close to a label, so as to obtain the parameters of the model; calculating a beam forming index and a channel gain of each user by utilizing statistical channel information of all the users in a to-be-scheduled system, and generating a normalized input matrix of the model; predicting the probability that each user is scheduled on line by the trained model, determining the corresponding number of users with the maximum probability value as the users to be served by the system, and obtaining a scheduling result.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a massive MIMO downlink user scheduling method based on deep learning. Background technique [0002] With the vigorous development of the mobile Internet and the rapid popularization of smart terminals, the amount of wireless communication data has grown exponentially, and people have put forward higher requirements for transmission quality and system capacity for wireless communication systems. Massive multiple-input multiple-output (MIMO) transmission technology is one of the key technologies in 5G communication systems. This technology replaces multi-antenna arrays with large-scale antenna arrays to obtain higher spectral efficiency and transmission reliability. However, as the number of antennas increases, it is very difficult for the base station to obtain complete channel state information in a timely manner. In recent years, researchers have carried out ef...

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Problems solved by technology

[0003] In addition, the maximum approximation and rate scheduling method based on statistical CSI is an iterative exhaustive search algorithm. With the increase of the number of users in the system, its computational complexity increases exponentially. Traditional calculation methods require a large number of computing resources and high computational latency

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