TY - GEN
T1 - An MCMC approach to multisensor linear modulation classification
AU - Ozdemir, Onur
AU - Theagarajan, Lakshmi N.
AU - Agarwal, Mohit
AU - Wimalajeewa, Thakshila
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - 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.
AB - 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.
KW - Artificial noise
KW - Bayesian sampling
KW - MCMC
KW - Modulation classification
KW - Multisensor systems
UR - http://www.scopus.com/inward/record.url?scp=85019694596&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019694596&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2017.7925864
DO - 10.1109/WCNC.2017.7925864
M3 - Conference contribution
AN - SCOPUS:85019694596
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Y2 - 19 March 2017 through 22 March 2017
ER -