Hysteretic hopfield neural network detection of multi-user CDMA signals

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Abstract

Multi-user reception using Hopfield Neural Network (HNN) and Hysteretic Hopfield Neural Network (HHNN) for Multi-Carrier Code Division Multiple Access (MC-CDMA) is investigated. We have shown that by the appropriate choice of Hopfield Neural Network parameters from the communication system, Hopfield Neural Network can collectively resolve multiple-access interference in the system. The "hysteretic" Hopfield Neural Network detector performance is investigated and compared in various asynchronous, Gaussian and multipath channel conditions with respect to varying near-far conditions expressed as E<inf>i</inf>/E<inf>1</inf>. The investigations have shown that the application of hysteresis provides some performance improvement in terms of BER. The BER improvement provided by hysteresis is more noticable at higher desired signal to noise ratios E<inf>1</inf>/N<inf>o</inf>. © 2005 IEEE.

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IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005 --

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Hysteretic Hopfield Neural Network (HHNN), Multi-Carrier Code Division Multiple Access (MC-CDMA), Multipath channel conditions, Code division multiple access, Communication systems, Hysteresis, Parameter estimation, Signal processing, Neural networks

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2005

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