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

On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers
On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers
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.
1-8
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harrison, Christine J.
52da7673-509c-4b88-b92e-0c021c9c7d3e
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harrison, Christine J.
52da7673-509c-4b88-b92e-0c021c9c7d3e

Chen, Sheng, Hong, Xia and Harrison, Christine J. (2014) On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers. 2014 IEEE World Congress on Computational Intelligence, China. 06 - 11 Jul 2014. pp. 1-8 .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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More information

e-pub ahead of print date: 6 July 2014
Venue - Dates: 2014 IEEE World Congress on Computational Intelligence, China, 2014-07-06 - 2014-07-11
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 365482
URI: http://eprints.soton.ac.uk/id/eprint/365482
PURE UUID: ca834fe9-893c-4106-94e6-1c2b35004ecc

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Date deposited: 05 Jun 2014 15:12
Last modified: 19 Jul 2019 21:11

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