Evolutionary-Algorithm-Assisted Joint Channel Estimation and Turbo Multiuser Detection/Decoding for OFDM/SDMA

The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantita...

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Vydáno v:	IEEE transactions on vehicular technology Ročník 63; číslo 3; s. 1204 - 1222
Hlavní autoři:	Jiankang Zhang, Sheng Chen, Xiaomin Mu, Hanzo, Lajos
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.03.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Applied sciences Channel estimation Channels Coding, codes Decoders Decoding Detection, estimation, filtering, equalization, prediction Differential evolution algorithm (DEA) Evolutionary algorithms evolutionary algorithms (EAs) Exact sciences and technology genetic algorithm (GA) Information, signal and communications theory Iterative decoding joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding Joints Mud Multiplexing Multiuser detection OFDM Optimization Orthogonal Frequency Division Multiplexing orthogonal frequency-division multiplexing (OFDM) particle swarm optimization (PSO) repeated weighted boosting search (RWBS) Searching Signal and communications theory Signal, noise space-division multiple access (SDMA) Swarm intelligence Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) Performance evaluation Parameter estimation Bit error rate Evolutionary algorithm Optimization method repeated weighted boosting search (RWBS) joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding Biomimetics evolutionary algorithms (EAs) Channel estimation Algorithm complexity space-division multiple access (SDMA) particle swarm optimization (PSO) Signal detection Cramer Rao inequality Multiuser detection Decoding circuit Turbo code Particle swarm optimization Maximum likelihood decoding Joint detection Differential evolution algorithm (DEA) Space division multiple access Genetic algorithm Optimal solution Orthogonal frequency division multiplexing genetic algorithm (GA) Maximum likelihood Comparative study orthogonal frequency-division multiplexing (OFDM)
ISSN:	0018-9545, 1939-9359
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Popis
Shrnutí:	The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoder's complexity.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0018-9545 1939-9359
DOI:	10.1109/TVT.2013.2283069