Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

Details Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

2010-12-24

arxiv.org/abs/1012.5327v3

Computer Science

Information Theory

3 pages, resubmitted to IEEE Communication Letters (modified based on reviewer comments)

Scientific paper

10.1109/LCOMM.2011.032811.110316

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.

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