Maximum likelihood detection method and device in multi-input multi-output system

Abstract

The invention provides a maximum likelihood detection method and a device in a multi-input multi-output system. The method comprises the following steps: A. carrying out QR decomposition on a channel matrix to obtain a first Q matrix and a first R matrix; B. carrying out QR decomposition on rows of the channel matrix after exchanging the sequence so as to obtain a second Q matrix and a second R matrix; C. selecting the R matrix with the absolute valves of diagonal elements of being arranged according to the sequence from small to large from the first R matrix and the second R matrix as a quenching R matrix; D. by receiving signals, exchanging R matrix and sequencing the Q matrix corresponding to the R matrix, calculating demodulated signals; and E. carrying out maximum likelihood searching according to the demodulated signals. The invention can effectively reduce the algorithm complexity of MIMO detection when the number of the receiving antennas is 2.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a maximum likelihood detection method and device in a multiple-input multiple-output (MIMO) system. Background technique [0002] MIMO maximum likelihood (ML) detection can enable the system to obtain the best bit error rate performance, but the ergodic search is often difficult to realize in real time or cannot be realized in the actual system due to its non-deterministic polynomial (NP) operation complexity. Therefore, Low-complexity MIMO-ML algorithms have always been the focus of research. Therefore, based on the research of Pohst et al., Viterbo et al. proposed a detection algorithm called spherical decoding (SD) for the source signal with a grid-like constellation diagram. [0003] The essence of sphere decoding is to construct the MIMO-ML detection problem as a problem of searching for an optimal path on a source signal constellation point tree,...

AI Technical Summary

Problems solved by technology

Although this method solves the problem that the selection of the K value of each layer is NP, it brings some performance loss. At the same time, the sorting of the Euclidean distance of nodes in each layer is also a very computationally intensive process. Efficient sorting is adopted The algorithm becomes the key factor to reduce the complexity of the algorithm

However, for MIMO detection of a terminal with only two receiving antennas, the algorithm itself is sometimes a bit outweighed.

[0006] Based on the problems of the above-mentioned different sphere decoding methods, especially for the case where there are only two receiving antennas, no matter which of the above-mentioned sphere decoding algorithms is adopted, some preprocessing before sphere decoding and addresses during the sphere decoding process are required. , Euclidean distance and other data updates, the algorithm complexity is still high

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