Efficient Massive MIMO Detection for M-QAM Symbols

Massive multiple-input multiple-output (MIMO) systems significantly outperform small-scale MIMO systems in terms of data rate, making them an enabling technology for next-generation wireless systems. However, the increased number of antennas increases the computational difficulty of data detection, necessitating more efficient detection techniques. This paper presents a detector based on joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations for rectangular m-ary quadrature amplitude modulation (M-QAM) symbols. Deregularization maximized the energy of the solution. With the box-constraint, the deregularization forces the solution to be close to the rectangular boundary set. The numerical results demonstrate that the proposed detector achieves a considerable performance gain compared to existing detection algorithms. The performance advantage increases with the system size and signal-to-noise ratio.

Automated summary

Massive multiple-input multiple-output (MIMO) systems outperform small-scale MIMO systems in terms of data rate, making them crucial for next-generation wireless systems. However, the increased number of antennas poses computational challenges for data detection, thus requiring more efficient techniques. This paper introduces a detector based on joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations for rectangular m-ary quadrature amplitude modulation (M-QAM) symbols. The proposed detector achieves significant performance improvement compared to existing algorithms, with the advantage increasing with system size and signal-to-noise ratio.