Matrix computations (3rd ed.)
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Advanced Wireless Communications: 4G Technologies
Advanced Wireless Communications: 4G Technologies
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
3G Evolution, Second Edition: HSPA and LTE for Mobile Broadband
3G Evolution, Second Edition: HSPA and LTE for Mobile Broadband
IEEE Transactions on Communications
Mitigation of radio interference in xDSL transmission
IEEE Communications Magazine
Equalization for DMT based broadband modems
IEEE Communications Magazine
ADSL: a new twisted-pair access to the information highway
IEEE Communications Magazine
An overview of limited feedback in wireless communication systems
IEEE Journal on Selected Areas in Communications
Channel estimation in a DMT based power-line communication system using sparse Bayesian regression
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
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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.