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

Abstract

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.

Efficient Massive MIMO Detection for M-QAM Symbols

“…Neumann series expansion is applied to approximate matrix inversion using a series of matrix–vector multiplications, which is simple to implement in hardware but converges slowly [ 7 ]. Newton iteration [ 8 ] and Chebyshev iteration [ 9 ] have been successively proposed to accelerate convergence. However, when the number of iterations exceeds one, their computational complexity exceeds that of exact matrix inversion [ 10 ].…”