Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk

Roy Karasik, Osvaldo Simeone, Hyeryung Jang, Shlomo Shamai Shitz

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst channel conditions. Targeting ultra-reliable communication scenarios, this paper takes a complementary approach by introducing the conditional value-at-risk (CVaR) rate as the expected rate of a worst-case fraction of clients. To support differential quality-of-service (QoS) levels in this class of clients, layered division multiplexing (LDM) is applied, which enables decoding at different rates. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled offline. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and the sample complexity is shown to increase as the designated fraction of worst-case clients decreases. Considering this theoretical result, meta-learning is introduced as a means to reduce sample complexity by leveraging data from previous deployments. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves a CVaR rate close to that achieved when the transmitter knows the fading distribution; and that meta-learning can significantly reduce data requirements.

Original languageEnglish (US)
Pages (from-to)8060-8074
Number of pages15
JournalIEEE Transactions on Communications
Volume70
Issue number12
DOIs
StatePublished - Dec 1 2022

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Broadcasting/multicasting
  • CVaR
  • LDM
  • meta-learning
  • ultra-reliable communication

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