Compressed sensing channel estimation method based on block comparison reconstruction

Abstract

The invention discloses a compressed sensing channel estimation method based on block comparison reconstruction. The compressed sensing channel estimation method comprises the following steps: acquiring a channel sub-matrix from an original channel matrix in a matrix block coding mode; performing two-dimensional discrete Fourier transform on the channel sub-matrix to obtain a frequency spectrum matrix; performing compressive sensing on the vector form of the frequency spectrum matrix to obtain a mathematical relationship between an observation value vector and the frequency spectrum matrix; reconstructing the observation value vector through a compressed sampling matching pursuit CoSaMP algorithm, and restoring the frequency spectrum of the channel sub-matrix; selecting appropriate elements and positions in the frequency spectrum matrix through adoption of a block comparison method, and calculating channel parameters corresponding to the effective path. According to the invention, the compressed sensing is carried out on the frequency spectrum of the channel sub-matrix and only the channel parameters on a limited number of effective paths need to be solved, so that the calculation scale is reduced to a great extent, and the length of the training sequence is further shortened and the training overhead is reduced because the channel sub-matrix is obtained by adopting a matrix block coding mode.

Description

technical field [0001] The invention belongs to the field of millimeter wave channel estimation, and specifically discloses a compression sensing channel estimation method based on block comparison and reconstruction. Background technique [0002] In recent years, due to the surplus resources of the millimeter wave frequency band and the shorter wavelength of the millimeter wave are very suitable for the application of large-scale antenna technology, the research on the millimeter wave MIMO system has become a hot spot in the field of wireless communication. The application of millimeter wave systems has penetrated into all aspects of people's lives, including automotive radar systems, satellite interstellar communications, mobile cellular communication systems, unified wireless high-definition multimedia interface technology, medical equipment transmission imaging and many other aspects. Therefore, the performance evaluation of the millimeter wave communication system has b...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, the millimeter wave communication system basically uses large-scale antenna technology, so the dimension of the channel matrix is ​​generally large. If the traditional compressed sensing channel estimation algorithm is used, a large number of observations will lead to a long training sequence. , and when the signal reconstruction method is used to restore the entire channel matrix, the sparseness K of the sparse vector used in the calculation will be relatively large, and the operation of the algorithm will require a large amount of calculation

All in all, the training overhead and time cost are relatively high

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