Research Article Open Access

AUTOMATIC MODULATION CLASSIFICATION USING STATISTICAL FEATURES IN FADING ENVIRONMENT

Abstract

Software radio technology is expected to play an important role in the development of Fourth Generation (4G) wireless communication systems. The signal identification process, an intermediate step between signal interception and demodulation, is a major task of an intelligent receiver. Automatic Modulation Classification (AMC) is the process of deciding, based on observations of the received signal, what modulation is being used at the transmitter. Ten Candidate signals 2ASK, 4ASK, 2PSK, 4PSK 2FSK, 4FSK and 16 QAM, GMSK, 64QAM and 256 QAM were generated. Channel conditions were modelled by simulating AWGN and multipath Rayleigh fading effect. Instantaneous features such as amplitude, phase and frequency were first derived. Stochastic features were derived from instantaneous features. Seven key features were used to develop the classifier. Higher order QAM signals such as 64QAM and 256 QAM were classified using higher order statistical parameters such as moments and cumulants. Decision Tree classifier was developed based on threshold values. Overall classification result obtained for SNR=3dB was more than 97 %. The success rate was around 99 % (no fading condition) for SNR=5dB. The developed classifier could classify ten modulated signals under varying channel conditions for SNR as low as -5dB.

Jaspal Bagga, Neeta Tripathi

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