Learning to Broadcast with Layered Division Multiplexing

Roy Karasik, Osvaldo Simeone, Shlomo Shamai Shitz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A broadcast/multicast communication system is studied in which layered division multiplexing (LDM) is applied to support differential quality-of-service (QoS) levels. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled during deployment. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and is shown to be monotonically decreasing as the dataset grows larger. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; and that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves an expected rate close to that achieved when the transmitter knows the fading distribution.

Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2696-2701
Number of pages6
ISBN (Electronic)9781665421591
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: Jun 26 2022Jul 1 2022

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2022-June
ISSN (Print)2157-8095

Conference

Conference2022 IEEE International Symposium on Information Theory, ISIT 2022
Country/TerritoryFinland
CityEspoo
Period6/26/227/1/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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