Benchmarking capabilities of evolutionary algorithms in joint channel estimation and turbo multi-user detection/decoding
Authors: Zhang, J., Chen, S., Mu, X. and Hanzo, L.
Journal: 2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages: 3354-3362
ISBN: 9781479904549
DOI: 10.1109/CEC.2013.6557981
Abstract:Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder. © 2013 IEEE.
Source: Scopus
Benchmarking Capabilities of Evolutionary Algorithms in Joint Channel Estimation and Turbo Multi-User Detection/Decoding
Authors: Zhang, J., Chen, S., Mu, X. and Hanzo, L.
Journal: 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Pages: 3354-3362
ISBN: 978-1-4799-0453-2
Source: Web of Science (Lite)