Neural Computation
Multirate systems and filter banks
Multirate systems and filter banks
The nature of statistical learning theory
The nature of statistical learning theory
Multiuser Detection
CDMA for Wireless Personal Communications
CDMA for Wireless Personal Communications
Adaptive minimum-BER decision feedback equalisers for binary signalling
Signal Processing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Constrained minimum-BER multiuser detection
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Adaptive Bayesian multiuser detection for synchronous CDMA withGaussian and impulsive noise
IEEE Transactions on Signal Processing
Adaptive minimum-BER linear multiuser detection for DS-CDMA signalsin multipath channels
IEEE Transactions on Signal Processing
Probability of error in MMSE multiuser detection
IEEE Transactions on Information Theory
Channel equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
New nonleast-squares neural network learning algorithms for hypothesis testing
IEEE Transactions on Neural Networks
Reduced RBF centers based multi-user detection in DS-CDMA systems
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Wireless Personal Communications: An International Journal
Block PIC technique for synchronous CI/MC-CDMA system using neural network
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Wireless Personal Communications: An International Journal
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Adaptive training of neural networks is typically done using some stochastic gradient algorithm that aims to minimize the mean square error (MSE). For many classification applications, such as channel equalization and code-division multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A nonlinear adaptive near minimum error rate algorithm called the nonlinear least bit error rate (NLBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to downlink multiuser detection in CDMA communication systems. Simulation results show that the NLBER algorithm has a good convergence speed and a small-size radial basis function network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square algorithm, although generally converging well in the MSE, can produce a poor error rate performance.