Large-scale MIMO channel feedback method based on PCA evolution

Abstract

The invention provides a large-scale MIMO channel feedback method based on PCA evolution. The method comprises the steps of firstly acquiring related characteristic parameter of a downlink channel and building a channel model in view of channel characteristics in a space domain and a frequency domain; secondly, clustering channel state information after vectorization, and improving correlation and compression of the channel state information thus reducing high-dimensional channel state information to low dimension; then, computing a covariance matrix and a sparse matrix of the channel state information in each cluster of a characteristic parameter channel model at a receiving and a transmitting end; further extracting a main component of a sparse vector via a selection matrix at the receiving end, thus allowing the selected sparse channel vector to meet a principle that the sum of each element is maximum; and at last, feeding the selected parse channel vector and a codeword index of the selection matrix back to the transmitting end. The method provided by the invention reduces a lot of feedback quantities and computation complexity while acquiring relative high accuracy based on a PCA thought and a clustering thought.

Description

technical field [0001] The present application relates to a channel state information feedback method in the technical field of wireless communication, in particular to a PCA evolution-based massive MIMO channel state information compression feedback method. Background technique [0002] Multiple-Input Multiple-Output (MIMO) technology refers to the use of multiple transmitting antennas and receiving antennas at the transmitting end and receiving end, respectively, so that signals are transmitted and received through multiple antennas at the transmitting end and receiving end, thereby improving communication quality. It can make full use of space resources, realize multiple transmissions and multiple receptions through multiple antennas, and can double the system channel capacity without increasing spectrum resources and antenna transmission power. It is regarded as the core technology of the next generation of mobile communications. Orthogonal Frequency Division Multiplexi...

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Problems solved by technology

However, most of the proposed methods for compressing channel information using PCA only consider the correlation in the space domain or the frequency domain, and the current selection matrix for principal component extraction needs to be dynamically updated, which undoubtedly increases feedback from the other hand. volume and increased computational complexity

[0005] To sum up, the channel state information feedback methods in massive MIMO systems that have appeared now, aiming at the problems of high computational complexity, large amount of feedback, and low feedback accuracy, etc., a large-scale MIMO-OFDM based on the evolution of principal component analysis is proposed. The channel information feedback method considers the correlation of channel information in the two dimensions of space and frequency domains, and combines the idea of ​​clustering to compress and feed back a large amount of channel state information to the transmitter and perform reconstruction operations

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