NDLI: Performance analysis of Automatic Modulation Classification in multipath fading environment

Content Provider	IEEE Xplore Digital Library
Author	Wallayt, W. Younis, M.S. Imran, M. Shoaib, M.
Copyright Year	2014
Description	Author affiliation: Nat. Univ. of Sci. & Technol., Islamabad, Pakistan (Wallayt, W.; Younis, M.S.) \|\| Coll. of Comput. & Inf. Sci., King Saud Univ., Riyadh, Saudi Arabia (Imran, M.; Shoaib, M.)
Abstract	Automatic Modulation Classification (AMC) is extremely desirable to realize the merits of cognitive radios in plethora of commercial and military applications. However, AMC is extremely challenging in real-world scenarios due to multipath fading and additive Gaussian noise on modulation schemes. Moreover, it becomes more difficult in blind environments where a little or no priori information about the received signal is available. Most of the available modulation classifiers do not consider the fading effects which results in performance degradation of classification in a blind channel environment. In this paper, we investigate the multipath fading effects on the AMC of some common modulation schemes i.e., BPSK, QPSK and 16-QAM for blind channels. In our work channel is assumed to be suffering from multipath and excessive additive noise resulting in low SNR of signal. The unknown channel and noise parameters are estimated using Hidden Markovian based Expectation Maximization algorithm. The estimated channel coefficients are then used in Maximum-Likelihood classifier for the classification of modulation scheme. Simulation results show that phase modulation schemes (i.e., BPSK and QPSK) perform better at low SNR compared to 16-QAM which includes the phase amplitude information.
Sponsorship	Malaysia Sect. CAS Chapt.
Starting Page	1
Ending Page	4
File Size	250724
Page Count	4
File Format	PDF
e-ISBN	9781479946532
DOI	10.1109/ICIAS.2014.6869545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-03
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Frequency Selective fading Automatic Modulation Classification Channel estimation Hidden Markov models Blind channel estimation Binary phase shift keying Expectation Maximization algorithm Maximum Likelihood classification Signal to noise ratio
Content Type	Text
Resource Type	Article

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