MIMO link self-adaptive transmission method based on machine learning

Abstract

The invention discloses a MIMO link self-adaptive transmission method based on machine learning, feature extraction and dimension reduction are carried out by using an unsupervised learning self-coding algorithm, the idea of deep learning is introduced, and the feature dimension and the calculation complexity can be reduced on the premise that the main information state information is reserved. According to the method, the mapping relation between the channel state information and the transmission parameters is constructed by utilizing a logistic regression algorithm, which is different from the previous fixed parameterization model; training can be carried out on the basis of sample data; under the condition that the quality of the data set is better and all the states are covered, the mapping relation between the channel state information and the transmission parameters can be better established; the channel state information can be more fully utilized compared with a traditional single equivalent signal-to-noise ratio. In addition, CQI selection is performed based on the channel matrix; the MIMO link self-adaptation method based on machine learning is not restricted by the receiver design through the channel matrix and the noise variance research; and universality is facilitated.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a machine learning-based MIMO link adaptive transmission method. Background technique [0002] Rapid and significant fluctuations are an important feature of instantaneous channels, and dynamically adjusting transmission modes for changing channel conditions is the key to achieving stability and transmission efficiency in wireless systems. The adaptive transmission technology dynamically selects transmission parameters according to the current channel state information (CSI, Channel State Information) so as to achieve higher throughput while maintaining transmission stability. Multiple key transmission parameters need to be determined in current and future transmission systems, such as transmit power, modulation and coding scheme (MCS, Modulation and Coding Scheme), in multi-antenna transmission and multi-antenna reception (MIMO, Multiple-Input-Multiple-Output) sys...

AI Technical Summary

Problems solved by technology

However, due to the coupling of various transmission factors such as users, mode selection, precoding, etc., and with the application of massive MIMO, it is extremely challenging to achieve link adaptation in transmission systems

[0003] Traditional link adaptation is a one-dimensional problem corresponding to a single SNR (Signal to Noise Ratio) and MCS, but due to the high dimensionality of channel state information in multi-channel transmission, a single SNR cannot effectively describe MIMO or normal The channel state of OFDM (Orthogonal Frequency Division Multiplexing), that is, the average SNR cannot provide enough information to determine the ideal transmission mode

Traditional machine learning-based methods often use raw data as feature sets, without feature extraction and dimensionality reduction to remove redundant information, resulting in a significant increase in system complexity and dimensionality

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