DocumentCode :
104988
Title :
Optimal Discriminant Functions Based on Sampled Distribution Distance for Modulation Classification
Author :
Urriza, Paulo ; Rebeiz, Eric ; Cabric, Danijela
Author_Institution :
Dept. of Electr. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
Volume :
17
Issue :
10
fYear :
2013
fDate :
Oct-13
Firstpage :
1885
Lastpage :
1888
Abstract :
In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution distance of the signal features.
Keywords :
modulation; Bayesian decision criteria; cumulative distribution function; low complexity approach; minimum classification error; modulation classification; optimal discriminant functions; sampled distribution distance; testpoint locations; Accuracy; Bayes methods; Computational complexity; Modulation; Signal to noise ratio; Vectors; Automatic modulation classification; Bhattacharyya distance; goodness-of-fit;
fLanguage :
English
Journal_Title :
Communications Letters, IEEE
Publisher :
ieee
ISSN :
1089-7798
Type :
jour
DOI :
10.1109/LCOMM.2013.082113.131131
Filename :
6587865
Link To Document :
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