Title :
On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers
Author :
Sheng Chen ; Xia Hong ; Harris, Chris J.
Author_Institution :
Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK
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 beam-former outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.
Keywords :
Gaussian processes; adaptive signal processing; antenna arrays; array signal processing; error statistics; mixture models; radio receivers; space division multiple access; OGMDE; adaptive minimum bit error rate beamforming receivers; online Gaussian mixture density estimator; probability density function; space division multiple access systems; Adaptive systems; Array signal processing; Bit error rate; Kernel; Least squares approximations; Receivers; Vectors;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889361