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On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers

Chen, S., Hong, X. and Harris, C. J. (2014) On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers. In: 2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, 2014, Beijing, China.

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Official URL: http://dx.doi.org/10.1109/IJCNN.2014.6889361

Abstract/Summary

We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate adaptive minimum bit-error-rate (MBER) beamforming receiver for multiple antenna based space-division multiple access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer’s output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer’s weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beamformer outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:39731

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