Abstract In this study, the performance of the proposed rece iver with the neural network Multiple Access Interference (MAI) detector is compared with the matched filter bank ( classical receiver), neural network that detects user's signal and single user bound for Additive White Gaussian Noise (AWGN) and Rayleigh fading asynchronous channels by computer simulations. There are a lot of study in the literature that com pare the neural network receiver and other methods. These neural network receivers detect the user bits after the matched fi lter. In this study, MAI is detected after the matched filter with the proposed neural network receiver and then user bits are obta ined by subtracting MAI from the matched filter out put. The proposed receiver with the neural network MAI detector has got better Bit Error Rate (BER) performance than th e neural network that detects user`s signal in AWGN and Rayleigh fading a synchronous channels for Signal Noise Ratio (SNR) simulations, and in AWGN asynchronous channels for the number of users simulations, although both have the same complexity. However, both have almost same BER performance in AWGN and Raylei gh fading asynchronous channels for Near Far Ratio (NFR) simulations, and in Rayleigh fading asynchronous ch annels for the number of users simulations.

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[22] Yoon S. and Rao S., Annealed Neural Network Based Multiuser Detector in Code Division Multiple Access Communications, IEE Proceedings5Communications , vol. 147, no. 1, pp. 57"62, 2000. Necmi Taspinar received his BSc degree in electronic engineering from Erciyes University, Kayseri, Turkey in 1983, MS degree in electronic and telecommunications engineering from the Technical University of Istanbul, Turkey in 1988, and PhD degree in electronic engineering from Erciyes University, Turkey in 1991. From 1984 to 1992 he was a research assistant, during 1992"1996 he was an assistant professor, during 1996"2002 he was an associate professor, and since 2002 he has been full professor in the Department of Electrical and Electronic Engineering, Erciyes University, Turkey. His research interests include theory and applicati ons of error"control coding, multi"user detection, chan nel estimation, equalization, synchronization, CDMA and spread"spectrum systems, OFDM systems, MIMO systems and applications of neural networks, fuzzy logic, adaptive neuro"fuzzy inference system and heuristic approaches to communications problems. Yalcin Isik received his BSc degree in electronics engineering from Gazi University, MS Degree in electronics engineering from Hacettepe University and PhD degree electronics engineering of Erciyes University, Turkey in 1985, 1988 and 2006 respectively. He is now an instructor at Endustrial Electronic Program of Vocational High School at Selcuk University of Turkey. His current research interest includes multiuser detection in CDMA, neural networks and fuzzy systems.