Error probability bounds for Gaussian channels under maximal and average power constraints
| Autor: | Gonzalo Vazquez-Vilar |
|---|---|
| Přispěvatelé: | European Commission, Ministerio de Economía y Competitividad (España) |
| Rok vydání: | 2021 |
| Předmět: |
Block code
FOS: Computer and information sciences Average power constraint Reliability (computer networking) Computer Science - Information Theory Structure (category theory) Maximal power constraint Finite blocklength analysis Probability density function 02 engineering and technology Library and Information Sciences Constellation design Probability of error 0202 electrical engineering electronic engineering information engineering Applied mathematics Mathematics Computer Science::Information Theory Telecomunicaciones Information Theory (cs.IT) Transmitter 020206 networking & telecommunications Gaussian channel Channel coding Computer Science Applications Power (physics) Hypothesis testing Gaussian channels Meta-converse Information Systems |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname IEEE Transactions on Information Theory |
| DOI: | 10.1109/TIT.2021.3063311 |
| Popis: | This paper studies the performance of block coding on an additive white Gaussian noise channel under different power limitations at the transmitter. Lower bounds are presented for the minimum error probability of codes satisfying maximal and average power constraints. These bounds are tighter than previous results in the finite blocklength regime, and yield a better understanding on the structure of good codes under an average power limitation. Evaluation of these bounds for short and moderate blocklengths is also discussed. Comment: Submitted to the IEEE Transactions on Information Theory. This article was presented in part at the 2019 IEEE International Symposium on Information Theory, Paris, France (ISIT 2019) and at the 2020 International Z\"urich Seminar on Communication and Information, Z\"urich, Switzerland (IZS 2020) |
| Databáze: | OpenAIRE |
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