A method for designing hybrid precoder/combiner based on matrix factorization

Abstract

The invention belongs to the technical field of wireless communication, and discloses a method for designing a hybrid precoder / combiner based on matrix decomposition, which comprises the design of a precoder and a combiner in an analog domain and a digital domain. The design of the analog precoder is based on equal-gain transmission, and the interference between users is eliminated by zeroing theelements on the non-main diagonal of the baseband equivalent channel matrix. The simulation combiner is designed to obtain the maximum antenna array gain, and the orthogonal basis is selected by DFT for each user. The design of digital precoder and combiner is based on matrix block diagonalization. The interference between users is eliminated by matrix QR decomposition, and the stream of each useris parallelized by LDLH decomposition, so as to obtain the maximum spectral efficiency. Numerical results show that the hybrid precoding / combining algorithm can achieve higher spectral efficiency with lower implementation complexity.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a matrix decomposition-based hybrid precoding / merger design method suitable for a large-scale multi-user multi-input multi-output system. Background technique [0002] With the rapid development of the fifth generation mobile communication (The 5th Generation Mobile Communication, 5G) technology, various wireless data services and multimedia applications continue to emerge, and people put forward higher requirements for the capacity of the wireless communication system. As one of the most promising key technologies of 5G, Massive Multiple-Input Multiple-Output (Massive Multiple-Input Multiple-Output, Massive MIMO) can greatly improve system performance. For the realization of the Massive MIMO system, when using the all-digital precoding / combiner structure, each antenna needs to be equipped with a radio frequency link, which will lead to high system impl...

AI Technical Summary

Problems solved by technology

Generally speaking, joint optimization can achieve better system performance, but its implementation complexity is high; iterative optimization has lower computational complexity than joint optimization, but it will inevitably lead to some performance loss, and even some calculations Problems with too much complexity

[0003] In short, the current hybrid precoding / combiner design has a compromise between performance and computational complexity. How to design a hybrid precoding / combiner with the same performance as an all-digital precoding / combiner and low implementation complexity is an urgent problem to be solved. The problem

[0004] The difficulty and significance of solving the above technical problems: the traditional digital block diagonalization (BlockDiagonalization, BD) algorithm can eliminate the interference between users and obtain the maximum spectrum efficiency. , QR decomposition and LDL H Low Complexity Algorithms for Decomposition Operations

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