Recognizing FM, BPSK and 16-QAM using supervised and unsupervised learning techniques
Mohammad Bari, Awais Khawar, Milos I. Doroslovacki, T. Charles Clancy · 2015
In this paper, we explore the use of supervised and unsupervised machine learning for signal classification in the joint presence of AWGN, carrier offset, asynchronous sampling and symbol intervals and correlated fast fading. Three simple features are studied to classify frequency modulation, binary phase shift keying and 16 point quadrature amplitude modulation. Support vector machines and self-organizing maps are used to classify the signals.