Fast matrix inversion based on Chebyshev acceleration for linear detection in massive MIMO systems

To circumvent the prohibitive complexity of linear minimum mean square error detection in a massive multiple-input multiple-output system, several iterative methods have been proposed. However, they can still be too complex and/or lead to non-negligible performance degradation. In this letter, a Chebyshev acceleration technique is proposed to overcome the limitations of iterative methods, accelerating the convergence rates and enhancing the performance. The Chebyshev acceleration method employs a new vector combination, which combines the spectral radius of the iteration matrix with the receiver signal, and also the optimal parameters of Chebyshev acceleration have also been defined. A detector based on iterative algorithms requires pre-processing and initialisation, which enhance the convergence, performance, and complexity. To influence the initialisation, the stair matrix has been proposed as the first start of iterative methods. The performance results show that the proposed technique outperforms state-of-the-art methods in terms of error rate performance, while significantly reducing the computational complexity.

Automated summary

The proposed Chebyshev acceleration technique overcomes the limitations of iterative methods in a massive MIMO system, accelerating convergence rates and enhancing performance. This method utilizes a new vector combination, incorporating the spectral radius of the iteration matrix and the receiver signal, with defined optimal parameters for Chebyshev acceleration.