Stochastic Cellular Neural Network for CDMA Multiuser Detection

Authors:
Zhilu Wu;Nan Zhao;Yaqin Zhao;Guanghui Ren
Affiliations:
School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

A novel method for the multiuser detection in CDMA communication systems based on a stochastic cellular neural network (SCNN) is proposed in this paper. The cellular neural network (CNN) can be used in multiuser detection, but it may get stuck in a local minimum resulting in a bad steady state. The annealing CNN detector has been proposed to avoid local minima; however, the near-far effect resistant performance of it is poor. So, the SCNN detector is proposed here through adding a stochastic term in a CNN. The performance of the proposed SCNN detector is evaluated via computer simulations and compared to that of the conventional detector, the stochastic Hopfield network detector, and the Annealing CNN detector. It is shown that the SCNN detector can avoid local minima and has a much better performance in reducing the near-far effect than these detectors, as well as a superior performance in bit-error rate.