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- Sheng Chen, Bernard Mulgrew, Peter M. Grant
- IEEE Trans. Neural Networks
- 1993

The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function… (More)

- Sheng Chen, Ahmad K. Samingan, Bernard Mulgrew, Lajos Hanzo
- IEEE Trans. Signal Processing
- 2001

—The problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS)… (More)

- Sheng Chen
- Neurocomputing
- 2006

A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in the regression model with an individual regularization parameter, the ability for the orthogonal least squares model selection to produce a very sparse model… (More)

- Sheng Chen, Bernard Mulgrew, Steve McLaughlin
- IEEE Trans. Signal Processing
- 1993

A Bayesian solution is derived for digital communication channel equalization with decision feedback. This is an extension to the maximum a posteriori probability symbol-decision equalizer to include decision feedback. A novel scheme of utilizing decision feedback is proposed which not only improves equalization performance but also reduces computational… (More)

- Sheng Chen, Xia Hong, Christopher J. Harris, Paul M. Sharkey
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2004

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares… (More)

- Sheng Chen, Nurul Nadia Ahmad, Lajos Hanzo
- IEEE Trans. Wireless Communications
- 2005

—An adaptive beamforming technique is proposed based on directly minimizing the bit-error rate (BER). It is demonstrated that this minimum BER (MBER) approach utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains… (More)

- Yong Li, Mengjiong Qian, Depeng Jin, Pan Hui, Zhaocheng Wang, Sheng Chen
- IEEE Transactions on Mobile Computing
- 2014

To cope with explosive traffic demands on current cellular networks of limited capacity, Disruption Tolerant Networking (DTN) is used to offload traffic from cellular networks to high capacity and free device-to-device networks. Current DTN-based mobile data offloading models are based on simple and unrealistic network assumptions which do not take into… (More)

- Sheng Chen, Y. Wu, B. L. Luk
- IEEE Trans. Neural Networks
- 1999

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level.… (More)

- Shinya Sugiura, Sheng Chen, Lajos Hanzo
- IEEE Trans. Communications
- 2010

communication systems, where the concept of SM is extended to include both the space and time dimensions, in order to provide a general shift-keying framework. More specifically, in the proposed STSK scheme one out of í µí± dispersion matrices is activated during each transmitted block, which enables us to strike a flexible diversity and multiplexing… (More)

- Sheng Chen, Xia Hong, Christopher J. Harris
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2004

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test… (More)