Recursive Least Squares-Algorithm-Based Normalized Adaptive Minimum Symbol Error Rate Equalizer

The adaptive minimum symbol error rate (AMSER) equalizer is known to have better symbol error rate (SER) performance than the adaptive minimum mean square error equalizer. Furthermore, the normalized AMSER (NAMSER) equalizer outperforms the AMSER equalizer, which can be regarded as the improvement o...

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Bibliographic Details
Published in:IEEE communications letters 2023-01, Vol.27 (1), p.317-321
Main Authors: Zhang, Minhao, Wang, Yifan, Tu, Xingbin, Qu, Fengzhong, Zhao, Hangfang
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive equalizers
Algorithms
Approximation algorithms
Binary phase shift keying
constrained optimization
Convergence
Criteria
Equalizers
Errors
Least squares
minimum symbol error rate
Optimization
Quadrature amplitude modulation
recursive least squares
Symbols
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Summary:The adaptive minimum symbol error rate (AMSER) equalizer is known to have better symbol error rate (SER) performance than the adaptive minimum mean square error equalizer. Furthermore, the normalized AMSER (NAMSER) equalizer outperforms the AMSER equalizer, which can be regarded as the improvement of the normalized least mean square (NLMS) equalizer by incorporating the minimum SER (MSER) criterion. Inspired by that, we propose an improved recursive least squares-based NAMSER equalizer (RLS-NAMSER) that takes the advantage of faster convergence of the RLS algorithm over the NLMS algorithm. The RLS algorithm is first reconsidered from the perspective of optimization problem and an approximate RLS (ARLS) algorithm is proposed which converges faster than the NLMS algorithm. The RLS-NAMSER equalizer is then proposed by combining the ARLS equalizer with the MSER criterion. Simulation results show that the RLS-NAMSER equalizer has better convergence performance than the NAMSER equalizer while having nearly the same steady state performance as the NAMSER equalizer.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3199751