Neuware -Significant performance gains are achievable in wireless systems using a Multi-Input Multi-Output (MIMO) communication system employing multiple antennas.This architecture is suitable for higher data rate multimedia communications.One of the challenges in building a MIMO system is the tremendous processing power required at the receiver. MIMO Symbol detection involves detecting symbol from a complex signal at the receiver. Nature Inspired techniques for non-linear approximate MIMO detectors with a low complexity near-optimal performance is presented. The approach is particularly attractive as Swarm Intelligence (SI) is well suited for physically realizable, real- time applications, where low complexity and fast convergence is of absolute importance. Application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms is studied. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, it is established that Swarm Intelligence optimized MIMO detection algorithms gives near-optimal Bit Error Rate (BER) performance, thereby reducing the ML computational complexity significantlyBooks on Demand GmbH, �berseering 33, 22297 Hamburg 168 pp. Englisch.

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T�tulo: Efficient Signal Processing Techniques in Advanced Wireless Systems
Editor: LAP LAMBERT Academic Publishing Jul 2010
Fecha de publicaci�n: 2010
Idioma: Ingl�s
ISBN 10: 3838374029
ISBN 13: 9783838374024
Encuadernaci�n: Taschenbuch
Estado: Neu

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Significant performance gains are achievable in wireless systems using a Multi-Input Multi-Output (MIMO) communication system employing multiple antennas.This architecture is suitable for higher data rate multimedia communications.One of the challenges in building a MIMO system is the tremendous processing power required at the receiver. MIMO Symbol detection involves detecting symbol from a complex signal at the receiver. Nature Inspired techniques for non-linear approximate MIMO detectors with a low complexity near-optimal performance is presented. The approach is particularly attractive as Swarm Intelligence (SI) is well suited for physically realizable, real- time applications, where low complexity and fast convergence is of absolute importance. Application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms is studied. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, it is established that Swarm Intelligence optimized MIMO detection algorithms gives near-optimal Bit Error Rate (BER) performance, thereby reducing the ML computational complexity significantly

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