Name
Title | ||
|---|---|---|
Modulation classification based on Gaussian mixture models under multipath fading channel |
Abstract | ||
|---|---|---|
This paper considers the classification of digital modulation schemes in the presence of multipath fading channels and additive noise. A novel modulation recognition approach is proposed based on Gaussian Mixture Models (GMM). Our basic procedure involves parameter estimation using GMM to set up an offline database and then to classify the received signal into different modulation schemes based on the database by using Kullback-Leibler (K-L) Divergence. In order to mitigate the negative impact from multipath fading channels, an iterative Maximum A Posteriori (MAP)-based channel estimation is used in conjunction with the Expectation-Maximization (EM) algorithm. Furthermore, Gaussian approximation is carried out to decrease the computational complexity. Monte Carlo simulations are conducted to evaluate the performance of individual modulation scheme classification. Numerical results show that the proposed approach is capable of recognizing various modulated signals with improved performance under AWGN and multipath fading channels. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/GLOCOM.2012.6503737 | Global Communications Conference |
Keywords | Field | DocType |
AWGN channels,Monte Carlo methods,adaptive modulation,channel estimation,fading channels,iterative methods,maximum likelihood estimation,multipath channels,signal classification,AWGN,EM algorithm,GMM,Gaussian approximation,Gaussian mixture model,K-L divergence,Kullback-Leibler divergence,MAP-based channel estimation,Monte Carlo simulation,digital modulation scheme,expectation-maximization algorithm,iterative maximum a posteriori,modulation classification,modulation recognition approach,multipath fading channel,parameter estimation | Multipath propagation,Monte Carlo method,Pattern recognition,Computer science,Fading,Modulation,Artificial intelligence,Maximum a posteriori estimation,Estimation theory,Additive white Gaussian noise,Mixture model | Conference |
ISSN | ISBN | Citations |
1930-529X E-ISBN : 978-1-4673-0919-6 | 978-1-4673-0919-6 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gejie Liu | 1 | 9 | 2.60 |
| Xianbin Wang | 2 | 2365 | 223.86 |
| Nadeau, J. | 3 | 1 | 0.74 |
| Hai Lin | 4 | 434 | 52.81 |