Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Discriminant Functions Based On Sampled Distribution Distance
  for Modulation Classification   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 of the signal features.

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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex