Modulation classification in MIMO fading channels via expectation maximization with non-data-aided initialization

Non-data aided channel estimation is discussed in this paper to enable blind modulation classification in multiple-input multiple-output fading channels. The channel parameters are jointly estimated via expectation maximization under each modulation hypothesis. Instead of pilot symbols, the initialization of the channel matrix is achieved through a combination of fuzzy c-means clustering and maximum likelihood mapping. The estimated channel matrix and noise power enable the blind classification of modulations using a maximum likelihood classifier. Digital modulations are tested in simulation to validate the proposed classifier. The classifier is able to achieve excellent performance when SNR level is above 5 dB.