The Error Probability of Generalized Perfect Codes via the Meta-Converse

Autor: Albert Guillen i Fabregas, Sergio Verdu, Gonzalo Vazquez-Vilar
Přispěvatelé: European Commission, Ministerio de Economía y Competitividad (España), Guillen i Fabregas, Albert [0000-0003-2795-1124], Apollo - University of Cambridge Repository
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Perfect codes
perfect codes
Hamming bound
Computer Science - Information Theory
channel coding
Finite blocklength analysis
02 engineering and technology
Data_CODINGANDINFORMATIONTHEORY
finite blocklength analysis
Library and Information Sciences
Lossy compression
Upper and lower bounds
rate distortion theory
Separable space
Quasi-perfect codes
Rate distortion theory
Probability of error
Converse
hypothesis testing
0202 electrical engineering
electronic engineering
information engineering
joint source-channel coding
Joint source-channel coding
Mathematics
Probability measure
Discrete mathematics
Telecomunicaciones
Information Theory (cs.IT)
maximum likelihood decoding
Maximum likelihood decoding
020206 networking & telecommunications
Shannon theory
Channel coding
Computer Science Applications
Hypothesis testing
meta-converse
Meta-converse
quasi-perfect codes
Information Systems
Coding (social sciences)
Zdroj: e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
IEEE Transactions on Information Theory
DOI: 10.1109/TIT.2019.2906227
Popis: We introduce a definition of perfect and quasi-perfect codes for symmetric channels parametrized by an auxiliary output distribution. This notion generalizes previous definitions of perfect and quasi-perfect codes and encompasses maximum distance separable codes. The error probability of these codes, whenever they exist, is shown to coincide with the estimate provided by the meta-converse lower bound. We illustrate how the proposed definition naturally extends to cover almost-lossless source-channel coding and lossy compression.
Submitted to IEEE Transactions on Information Theory
Databáze: OpenAIRE
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