Performance of an ML Modulation Classification of QAM Signals with Single-Sample Observation


The KIPS Transactions:PartC, Vol. 12, No. 1, pp. 63-68, Feb. 2005
10.3745/KIPSTC.2005.12.1.63,   PDF Download:

Abstract

In this paper, performance of a maximum-likelihood modulation classification for quadrature amplitude modulation (QAM) is studied. Unlike previous works, the relative classification performance with respect to the available modulations and performance limit with single-sample observation are presented. For those purpose, all constellations are set to have the same minimum. Education distance between symbols so that a smaller constellation is a subset of the larger ones. And only one sample of received waveform is used for multiple hypothesis test. As a result, classification performance is improved with increase in signal-to-noise ratio in all the experiments. Especially, when the true modulation format used in the transmitter is 4 QAM, almost perfect classification can be achieved without any additional information or observation samples. Though the possibility of false classification due to the symbols shared by subset constellations always exists, correct classification ratio of 80% can be obtained with the single-sample observation when the true modulation formats are 16 and QAM.


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Cite this article
[IEEE Style]
S. G. Kang, "Performance of an ML Modulation Classification of QAM Signals with Single-Sample Observation," The KIPS Transactions:PartC, vol. 12, no. 1, pp. 63-68, 2005. DOI: 10.3745/KIPSTC.2005.12.1.63.

[ACM Style]
Seog Geun Kang. 2005. Performance of an ML Modulation Classification of QAM Signals with Single-Sample Observation. The KIPS Transactions:PartC, 12, 1, (2005), 63-68. DOI: 10.3745/KIPSTC.2005.12.1.63.