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Blind algorithms for multiple-input multiple-output (MIMO) signalsinterception have recently received considerable attention because of theirimportant applications in modern civil and military communication fields. Onekey step in the interception process is to blindly recognize the modulationtype of the MIMO signals. This can be performed by employing a ModulationClassification (MC) algorithm, which can be feature-based or likelihood-based.To overcome the problems associated with the existing likelihood-based MCalgorithms, a new algorithm is developed in this paper. We formulated the MCproblem as maximizing a global likelihood function formed by combining thelikelihood functions for the estimated transmitted signals, where Minimum MeanSquare Error (MMSE) filtering is employed to separate the MIMO channel intoseveral sub-channels. Simulation results showed that the proposed algorithmworks well under various operating conditions, and performs close to theperformance upper bound with reasonable complexity.

An Efficient Likelihood-Based Modulation Classification Algorithm for MIMO Systems