This work proposes a near-optimal hard output neural network based iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer, based on earlier work by the authors, able to equalize single carrier 4-QAM signals in underwater acoustic channels with extremely long delay spreads. The performance of the proposed equalizer is compared to a suboptimal equalization technique, namely Decision Feedback Equalization (DFE), via computer simulation for a number of power delay profiles. Results show unparalleled performance at a fraction of the computational cost of optimal, yet impractical, equalization methods. The superior computational complexity of the proposed equalizer is due to the high parallelism and high level of neuron interconnection of its foundational neural network structure.
Reference:
Myburgh, HC and Olivier, JC. 2009. Low complexity iterative MLSE equalization in highly spread underwater acoustic channels. OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany, 11-14 May 2009, pp 1-7
Myburgh, H., & Olivier, J. (2009). Low complexity iterative MLSE equalization in highly spread underwater acoustic channels. Institute of Electrical and Electronic Engineers (IEEE). http://hdl.handle.net/10204/3880
Myburgh, HC, and JC Olivier. "Low complexity iterative MLSE equalization in highly spread underwater acoustic channels." (2009): http://hdl.handle.net/10204/3880
Myburgh H, Olivier J, Low complexity iterative MLSE equalization in highly spread underwater acoustic channels; Institute of Electrical and Electronic Engineers (IEEE); 2009. http://hdl.handle.net/10204/3880 .
Copyright: 2009 Institute of Electrical and Electronics Engineering (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ResearchSpace
