Title :
Sparse Bayesian Learning Approach to Adaptive Beamforming Assisted Receivers
Author :
Choi, Sooyong ; Chung, Jong-Moon
Author_Institution :
Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul
Abstract :
In this letter, a new adaptive beamforming assisted receiver based on sparse Bayesian learning is proposed. We consider a general probabilistic Bayesian learning framework for obtaining sparse solutions to adaptive beamforming assisted receivers to improve the performance of an adaptive beamforming assisted receiver based on the minimum mean squared error (MMSE) scheme. Simulation experiments show that the bit error rate (BER) performance of the sparse Bayesian beamforming receiver shows an outstanding BER performance compared to MMSE beamforming receivers
Keywords :
Bayes methods; array signal processing; error statistics; learning (artificial intelligence); least mean squares methods; probability; receivers; BER; MMSE scheme; adaptive beamforming assisted receiver; bit error rate; minimum mean squared error; probability; sparse Bayesian learning approach; Array signal processing; Bayesian methods; Bit error rate; Gaussian noise; Kernel; Machine learning; Sparse matrices; Support vector machine classification; Support vector machines; Wireless communication;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2007.050231
