Low-complexity soft ML detection for generalized spatial modulation

Generalized Spatial Modulation (GSM) is a recent Multiple-Input Multiple-Output (MIMO) scheme, which achieves high spectral and energy efficiencies. Specifically, soft-output detectors have a key role in achiev-ing the highest coding gain when an error-correcting code (ECC) is used. Nowadays, soft-output Maxi-mum Likelihood (ML) detection in MIMO-GSM systems leads to a computational complexity that is un-feasible for real applications; however, it is important to develop low-complexity decoding algorithms that provide a reasonable computational simulation time in order to make a performance benchmark available in MIMO-GSM systems. This paper presents three algorithms that achieve ML performance. In the first algorithm, different strategies are implemented, such as a preprocessing sorting step in order to avoid an exhaustive search. In addition, clipping of the extrinsic log-likelihood ratios (LLRs) can be incor-porating to this algorithm to give a lower cost version. The other two proposed algorithms can only be used with clipping and the results show a significant saving in computational cost. Furthermore clipping allows a wide-trade-off between performance and complexity by only adjusting the clipping parameter. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents three algorithms that achieve ML performance for MIMO-GSM systems. The first algorithm utilizes different strategies, including preprocessing sorting and clipping, to reduce computational complexity. The other two algorithms can only be used with clipping, but they show significant savings in computational cost.