Signal detection method suitable for large-scale MIMO system

Abstract

The invention discloses a signal detection method suitable for a large-scale MIMO system, and the method comprises the steps: S1, obtaining a channel matrix and a received signal, and carrying out thesorting of the channel matrix according to the column vector norm, so as to form a new channel matrix; S2, constructing a detection model based on the received signal and a new channel matrix; S3, onthe basis of the detection model, the received signal and the new channel matrix, calculating an initial estimation value of the transmitted signal; and S4, inputting the initial estimation value ofthe transmitted signal into a PIC detector to obtain a signal subjected to PIC detection, and performing iteration on the signal to obtain a final estimation value of the transmitted signal. The complexity of the algorithm can be effectively reduced, and the bit error rate is obviously lower than that of a traditional nonlinear signal detection algorithm.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a signal detection method applicable to a massive MIMO system. Background technique [0002] In recent years, with the explosive increase of mobile data traffic, the traditional multiple-input multiple-output (MIMO) technology has become increasingly popular because it can only provide 4×4 or 8×8 antenna scale systems. Powerless. Therefore, massive MIMO technology emerged as the times require. A massive MIMO system refers to the configuration of dozens or even hundreds of antenna arrays at the base station to serve multiple single-antenna user terminals at the same time, which greatly improves the spectrum and energy of the system. efficiency. However, as the number of antennas increases, massive MIMO systems also face several problems, one of which is how to achieve efficient and reliable uplink signal detection. [0003] With the large increase in ...

AI Technical Summary

Problems solved by technology

However, these linear detection algorithms involve complex matrix inversion operations resulting in high algorithm complexity

In order to reduce the computational complexity brought by matrix inversion, the Neumann series expansion algorithm is used for signal detection, but when the number of iterations is greater than 2, its computational complexity returns to O(k 3 ), and when the ratio of the number of base station antennas to user antennas is close to 1, it will bring obvious BER performance loss

GAO X proposed the Richardson iterative algorithm, but the performance of the algorithm is poor when the iterative parameter calculation is large and the number of iterations is low

TANG C and DAI L proposed the Gauss-Seidel algorithm and the Newton algorithm, but these two algorithms focus more on precision, so the computational complexity is relatively large

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