Generalization Bounds and Algorithms for Learning to Communicate Over Additive Noise Channels

An additive noise channel is considered, in which the distribution of the noise is nonparametric and unknown. The problem of learning encoders and decoders based on noise samples is considered. For uncoded communication systems, the problem of choosing a codebook and possibly also a generalized mini...

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Vydáno v:	IEEE transactions on information theory Ročník 68; číslo 3; s. 1886 - 1921
Hlavní autor:	Weinberger, Nir
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Additive noise channels Algorithms alternating optimization algorithm Coders Codes Communications systems Convergence Covariance matrix Decoders Decoding Entropy entropy estimation Error probability Errors expurgation generalization bounds Gibbs algorithm hinge loss Loss measurement Machine learning minimal distance decoding mismatch decoding Noise Optimization statistical learning stochastic gradient descent
ISSN:	0018-9448, 1557-9654
On-line přístup:	Získat plný text
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