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Low-complexity soft ML detection for generalized spatial modulation.
- Source :
-
Signal Processing . Jul2022, Vol. 196, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • Maximum-Likelihood detection for soft-output in MIMO-GSM systems leads a high computational cost. • Three different algorithms that achieve Maximum-Likelihood performance are proposed to provide a reasonable computational cost in order to make a performance benchmark available. • The algorithms show reduced complexity compared to other ML algorithms, especially when the system size increases. 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 achieving the highest coding gain when an error-correcting code (ECC) is used. Nowadays, soft-output Maximum Likelihood (ML) detection in MIMO-GSM systems leads to a computational complexity that is unfeasible 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 incorporating 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DECODING algorithms
*ERROR-correcting codes
*ENERGY consumption
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 196
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 155961270
- Full Text :
- https://doi.org/10.1016/j.sigpro.2022.108509