Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Chen, Sheng; Samingan, A.K.; Hanzo, Lajos

doi:10.1109/72.925563

Cited by 111 publications

(64 citation statements)

References 31 publications

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“…Otherwise, nonlinear filtering is required in order to achieve an adequate performance. Examples of such nonlinear filtering include nonlinear single-user channel equalisation [89][90][91][92][93][94][95][96][97][98][99][100][101], nonlinear CDMA multiuser detection [102], nonlinear beamforming assisted detection [103][104][105][106], and nonlinear space-time equalisation [107]. Let us consider the generic nonlinear filter of the form y R ðkÞ ¼ f ðxðkÞ; wÞ,…”

Section: Extension To Nonlinear Filteringmentioning

confidence: 99%

Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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Adaptive filtering has been an enabling technology and has found ever-increasing applications in various state-of-the-art communication systems. Traditionally, adaptive filtering has been developed based on the Wiener or minimum mean square error (MMSE) approach, and the famous least mean square algorithm with its low computational complexity readily meets the fast real-time computational constraint of modern high-speed communication systems. For a communication system, however, it is the system's bit error rate (BER), not the mean square error (MSE), that really matters. It has been recognised that minimising the MSE criterion does not necessarily produce the minimum BER (MBER) performance. The introduction of the novel MBER design has opened up a whole new chapter in the optimisation of communication systems, and its design trade-offs have to be documented in contrast to those of the classic but actually still unexhausted MMSE and other often-used optimisation criteria. This contribution continues this theme, and we provide a generic framework for adaptive minimum error-probability filter design suitable for the employment in a variety of communication systems. Advantages and disadvantages of the adaptive minimum error-probability filter design are analysed extensively, in comparison with the classic Wiener filter design. r

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Section: Extension To Nonlinear Filteringmentioning

confidence: 99%

Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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“…However, a linear detector only performs adequately if R þ and R À are linearly separable. If R þ and R À are not linearly separable, a linear detector will have a high BER floor even without noise and a nonlinear detector will be required [48]. Even in the case that R þ and R À are linearly separable, a nonlinear detector can often outperform a linear one considerably at a cost of increased complexity.…”

Section: Linear and Optimal Detectorsmentioning

confidence: 99%

“…Even in the case that R þ and R À are linearly separable, a nonlinear detector can often outperform a linear one considerably at a cost of increased complexity. Applying the maximum a posteriori probability principle, it can be shown that the optimal oneshot detector is the following Bayesian one [48]: …”

Section: Linear and Optimal Detectorsmentioning

confidence: 99%

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

2005

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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 codedivision 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. r

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“…We adopt the enhanced κ-means clustering algorithm [16], [19] to adapt the RBF centres. Specifically, during training, the RBF centres are adjusted according to (14) where µ c is the step size,…”

Section: B Downlink Detectionmentioning

confidence: 99%

“…The L-MBER beamforming outperforms the L-MMSE one, particularly in hostile rank-deficient scenarios. Digital communication signal detection however can be viewed as a classification problem [11], [12], [13], [14], where the receiver simply classifies the received channel-impaired signal into the most-likely transmitted symbol constellation point. For the multiple-antenna aided receiver, if one is willing to extend the beamforming process to nonlinear, substantial performance enhancement can be achieved over the linear beamforming at the cost of an increased complexity.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Beamforming for Multiple-Antenna Assisted QPSK Wireless Systems

Chen

Hanzo

Tan

2008

2008 IEEE International Conference on Communications

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Abstract-A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.

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Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Cited by 111 publications

References 31 publications

Adaptive minimum error-rate filtering design: A review

Adaptive minimum error-rate filtering design: A review

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

Nonlinear Beamforming for Multiple-Antenna Assisted QPSK Wireless Systems

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