Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Downlink Massive Random Access with Lossy Source Coding
 
conference paper

Downlink Massive Random Access with Lossy Source Coding

Song, Ryan  
•
Yu, Wei
2025
2025 IEEE International Symposium on Information Theory (ISIT)
2025 IEEE International Symposium on Information Theory

This paper considers the coded downlink massive random access problem in which a base-station (BS) aims to communicate descriptions of the sources (X1, ·s, Xk) to a randomly activated subset of k users, among a large pool of n potential users, via a common message in the downlink. Assuming that the downlink channel is noiseless, this paper investigates the lossy source coding setting where upon receiving the common message from the BS, each active user aims to recover a reconstruction Xi of their intended source Xi, such that the expected distortion between (X1, ·s, Xk) and (X1, ·s, Xk) is less than D. In this paper, we show that a previously proposed lossless coding strategy and its corresponding codebook construction for exchangeable sources, the urn codebook, can be extended to the lossy source coding setting using the Poisson functional representation. With this coding strategy, we show that for exchangeable sources (X1, ·s, Xk), a common message length of R(D) bits plus an overhead of O(k) bits, independent of n, is achievable, where R(D) is the rate-distortion function for compressing (X1, ·s, Xk). If the sources are i.i.d., this overhead can be reduced to O(log (k)) bits.

  • Details
  • Metrics
Type
conference paper
DOI
10.1109/ISIT63088.2025.11195677
Scopus ID

2-s2.0-105022004700

Author(s)
Song, Ryan  

École Polytechnique Fédérale de Lausanne

Yu, Wei

University of Toronto

Date Issued

2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Published in
2025 IEEE International Symposium on Information Theory (ISIT)
DOI of the book
https://doi.org/10.1109/ISIT63088.2025
ISBN of the book

9798331543990

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTHI  
Event nameEvent acronymEvent placeEvent date
2025 IEEE International Symposium on Information Theory

ISIT 2025

Ann Arbor, MI, USA

2025-06-22 - 2025-06-27

Available on Infoscience
November 24, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/256246
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés