A 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, including: acquiring group users, each group including statistical channel information of a user, calculating the beamforming index and channel gain of each user; using the maximum approximate sum rate The method performs user scheduling for each group of users to generate a training data; constructs and offline trains the convolutional neural network model of the user scheduling scheme of the prediction system, so that the scheduling probability vector of each user predicted by the model is close to the label to obtain the parameters of the model; Statistical channel information of all users in the scheduled system, calculate the beamforming index and channel gain of each user, and generate the normalized input matrix of the model; predict the probability of each user being scheduled online by the trained model, and use the largest probability value The corresponding user is determined as the user to be served by the system, and the scheduling result is obtained.

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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