Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1155/2010/191808 | J. Electrical and Computer Engineering |
Keywords | Field | DocType |
proposed rvm-based method,probabilistic bayesian,new channel estimation method,bayesian frame work,sparse solution,relevance vector machine,dmt-based system,sparse bayesian learning,sparse bayesian,frequency domain equalization,various channel estimation technique,proposed channel estimate,enhanced ber probability | Least squares,Bayesian inference,Equalization (audio),Pattern recognition,Computer science,Linear model,Artificial intelligence,Relevance vector machine,Probabilistic logic,Bit error rate,Bayesian probability | Journal |
Volume | ISSN | Citations |
2010, | 2090-0147 | 1 |
PageRank | References | Authors |
0.35 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashraf A. Tahat | 1 | 15 | 3.18 |
| Nikolaos P Galatsanos | 2 | 145 | 5.39 |