Modified Hopfield Neural Network for CDMA Multiuser Detection

Liu, Xiangdong; Wang, Xuexia; Wu, Zhilu; Gu, Xuemai

doi:10.1007/11760191_13

Cited by 3 publications

(1 citation statement)

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“…Based on that, reference [14] reduced the convergence time of the blind detector effectively by the Kennedy-Chua neural network, and Chen proposed a nonlinear adaptive algorithm called nonlinear least bit error rate (NLBER) [15], where the training procedure of the neural networks was improved. Liu et al also proposed a detection algorithm in [16] based on the modified Hopfield structure, and they claimed to be able to take advantage of the Hopfield network to perform fast gradient descent in the hardware implementation. The Quantum NN-MUD in [17] exhibited more powerful properties both in performance and in near-far effect resistance than the Hopfield network.…”

Section: Introductionmentioning

confidence: 97%

An Adaline Neural Network-Based Multi-User Detector Improved by Particle Swarm Optimization in CDMA Systems

Wang

Yang

et al. 2010

Wireless Pers Commun

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The detector applied to each single user based on adaline neural network (ANN) is presented in this paper, which is equivalent to the joint one applied to all users in eliminating the multiple access interference in CDMA systems. Then, particle swarm optimization (PSO) algorithm combined with least mean square scheme is employed in the training procedure of the ANN, which can effectively remove the shortcoming of the poor dynamic adaptive behavior of conventional neural network, i.e. during the convergence procedure of the weights in conventional neural network, the training samples are usually required to be trained iteratively. However, in the improved detector, each training sample can be trained repeatedly, so that the converging speed is getting much faster. Simulation results show that, in the ANN based multi-user detector improved by PSO, the dynamic adaptive behavior has been remarkably improved.

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