An MCMC approach to multisensor linear modulation classification

Onur Ozdemir, Lakshmi N. Theagarajan, Mohit Agarwal, Thakshila Wimalajeewa, Pramod Kumar Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Automatic modulation classification (AMC) with multiple sensors is a challenging problem when the channel conditions are unknown at the receiver. In this paper, using the Markov chain Monte Carlo (MCMC) approach, we develop a novel algorithm for AMC when the amplitude and phase of the channel gains are unknown. Using sampling techniques, we marginalize over the unknown channel parameters that follow a certain probability distribution. This improves the estimate of the a posteriori distribution of the modulation formats, thereby improving the overall classification performance. Further, to overcome the problem of local extrema traps encountered in sampling algorithms, we introduce the idea of adding artificial noise beyond a certain threshold of signal-to-noise (SNR). This improves the performance of the sampling based AMC algorithm in the high SNR regime. Simulation results and comparisons are provided to show the efficiency of the proposed algorithm over the most related works in the literature.

Original languageEnglish (US)
Title of host publication2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509041831
StatePublished - May 10 2017
Externally publishedYes
Event2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States
Duration: Mar 19 2017Mar 22 2017


Other2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Country/TerritoryUnited States
CitySan Francisco


  • Artificial noise
  • Bayesian sampling
  • MCMC
  • Modulation classification
  • Multisensor systems

ASJC Scopus subject areas

  • Engineering(all)


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