Efficient iterative massive MIMO detection using Chebyshev acceleration

Massive multiple-input multiple-output (MIMO) detection is one of the most important, yet complex parts of the fifth generation (5G) baseband receiver. The linear minimum mean square error (MMSE) signal detection achieves almost optimum efficiency when the number of antennas at the base station is asymptotically large. However, the matrix inversion required for MMSE can also be very complex when the number of users increases. In this paper, a low complexity signal detection algorithm based on modified accelerated overrelaxation (MAOR) method is proposed to iteratively approach the MMSE performance. We calculate optimal values of two key parameters of MAOR and also provide a suitable and less complex initial solution to accelerate the convergence. Furthermore, we adopt the Chebyshev polynomial acceleration technique to present the MAOR method with a new vector combinations, which enhances the performance of the detection algorithm. The spectral radius of MAOR is also calculated to demonstrate its suitability for Chebyshev acceleration. This complete solution is referred to as Chebyshev-MAOR. The results have revealed that the proposed method can achieve faster convergence and better performance than other state-of-the-art detection algorithms. It is also shown that Chebyshev-MAOR reduces computational complexity by an order of magnitude from O(K³) to O(K²), with K denoting the number of transmit antennas. Our performance results show that these complexity gains are achieved with negligible impact on the bit error rate (BER) performance.