Name
Title | ||
|---|---|---|
Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels |
Abstract | ||
|---|---|---|
In this paper, we consider distributed maximum likelihood (ML) classification of digital amplitude-phase modulated signals using multiple sensors that observe the same sequence of unknown symbol transmissions over nonidentical flat blockfading Gaussian noise channels. A variant of the expectation-maximization (EM) algorithm is employed to obtain the ML estimates of the unknown channel parameters and compute the global log-likelihood of the observations received by all the sensors in a distributed manner by means of an average consensus filter. This procedure is repeated for all candidate modulation formats in the reference library, and a classification decision, which is available at any of the sensors in the network, is declared in favor of the modulation with the highest log-likelihood score. The proposed scheme improves the classification accuracy by exploiting the signal-to-noise ratio (SNR) diversity in the network while restricting the communication to a small neighborhood of each sensor. Numerical examples show that the proposed distributed EM-based classifier can achieve the same classification performance as that of a centralized classifier, which has all the sensor measurements, for a wide range of SNR values. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TWC.2014.2359019 | Wireless Communications, IEEE Transactions |
Keywords | Field | DocType |
gaussian channels,expectation-maximisation algorithm,maximum likelihood decoding,maximum likelihood detection,average consensus filter,candidate modulation formats,classification decision,digital amplitude-phase modulated signals,distributed maximum likelihood classification,expectation-maximization algorithm,global log-likelihood,linear modulations,multiple sensors,nonidentical flat block-fading gaussian channels,reference library,unknown channel parameters,unknown symbol transmissions,distributed modulation classification,fading channels,maximum likelihood,wireless sensor networks,sensors,noise,maximum likelihood estimation,vectors,constellation diagram | Pattern recognition,Fading,Expectation–maximization algorithm,Modulation,Constellation diagram,Artificial intelligence,Estimation theory,Maximum likelihood sequence estimation,Wireless sensor network,Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 2 | 1536-1276 |
Citations | PageRank | References |
7 | 0.54 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berkan Dulek | 1 | 107 | 13.74 |
| Onur Ozdemir | 2 | 166 | 15.74 |
| Pramod K. Varshney | 3 | 6689 | 594.61 |
| Wei Su | 4 | 18 | 2.32 |