Title :
Detection of Multipath DS CDMA Signals Using Neural Networks
Author :
Soujeri, Ebrahim Ali ; Bilgekul, Hüseyin
Author_Institution :
Elektrik ve Elektronik Muhendisligi Bolumu, Lefke Avrupa Univ., Kibris
Abstract :
Multi-user interference (MAI) cancellation using hysteretic Hopfield neural network (HHNN) receiver for Direct Sequence Code-Division Multiple Access (DS-CDMA) in multipath fading channels is investigated. We have shown that by applying the phenomenon of "hysteresis" to the HNN detector, we may enhance the performance of this detector in all near-far situations, for different number of multipath rays. The introduction of hysteresis concept into HNN has made the CDMA HNN detector (abbreviated as HHNN) even closer to the CDMA optimum multiuser detector. As shown by simulation results, the BER performance achieved by the HHNN detector outperforms the classical HNN detector with a good margin and is promising
Keywords :
Hopfield neural nets; code division multiple access; error statistics; fading channels; interference suppression; multipath channels; signal detection; spread spectrum communication; telecommunication computing; BER performance; DS CDMA signal detection; HHNN receiver; MAI cancellation; bit error rate; direct sequence code-division multiple access; hysteretic Hopfield neural network; multipath fading channel; multiuser interference; Bit error rate; Detectors; Fading; Gaussian processes; Hopfield neural networks; Hysteresis; Interference cancellation; Multiaccess communication; Multiple access interference; Neural networks;
Conference_Titel :
Signal Processing and Communications Applications, 2006 IEEE 14th
Conference_Location :
Antalya
Print_ISBN :
1-4244-0238-7
DOI :
10.1109/SIU.2006.1659809