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Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

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4 Author(s)
Paulo Urriza ; Department of Electrical Engineering, University of California, Los Angeles, 56-125B Engineering IV Building, Los Angeles, CA 90095-1594, USA ; Eric Rebeiz ; Przemyslaw Pawelczak ; Danijela Cabric

We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.

Published in:

IEEE Communications Letters  (Volume:15 ,  Issue: 5 )