An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework

2013 ◽  
Vol 20 (9) ◽  
pp. 889-892 ◽  
Author(s):  
Lifan Zhao ◽  
Guoan Bi ◽  
Lu Wang ◽  
Haijian Zhang
Keyword(s):  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Learning Framework ◽  
Calibration Algorithm

scholarly journals Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Ashraf A. Tahat ◽  
Nikolaos P. Galatsanos
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Linear Models ◽  
Estimation Method ◽  
Relevance Vector Machine ◽  
Sparse Bayesian Learning ◽  
Discrete Multitone ◽  
Learning Framework ◽  
Channel Estimate ◽  
Sparse Solutions

A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented. The Bayesian frame work is used to obtain sparse solutions for regression tasks with linear models. By exploiting a probabilistic Bayesian learning framework, sparse Bayesian learning provides accurate models for estimation and consequently equalization. We consider frequency domain equalization (FEQ) using the proposed channel estimate at both the transmitter (preequalization) and receiver (postequalization) and compare the resulting bit error rate (BER) performance curves for both approaches and various channel estimation techniques. Simulation results show that the proposed RVM-based method is superior to the traditional least squares technique.

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO
Keyword(s):  
Bayesian Learning ◽  
Blind Identification ◽  
Sparse Bayesian Learning ◽  
Multichannel Systems
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