Skip to main navigation Skip to search Skip to main content

Self-Supervised Zero-Shot Noise2Noise Framework for Improved Channel Estimation in RIS-Aided Multi-User Systems

  • Justine M. Mdali
  • , Mohammed Abo-Zahhad
  • , Ahmed H. Abd El-Malek
  • , Osamu Muta
  • , Maha Elsabrouty

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

Abstract

Accurate channel estimation is crucial for the proper operation of reconfigurable intelligent surfaces (RIS). This paper introduces a convolutional neural network (CNN) approach for multi-user RIS channel estimation that incorporates the zero-shot noise-to-noise (N2N) methodology within its architecture. In contrast to techniques that rely on clean training data, the proposed method learns from the noisy data itself to figure out how to remove the noise. The proposed zero-shot N2N self-learning demonstrates improved performance and a fast convergence rate in the RIS channel estimation.

Original languageEnglish
Title of host publication2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2024
PublisherIEEE Computer Society
Pages449-454
Number of pages6
ISBN (Electronic)9798350387445
DOIs
Publication statusPublished - 2024
Event20th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2024 - Paris, France
Duration: Oct 21 2024Oct 23 2024

Publication series

NameInternational Conference on Wireless and Mobile Computing, Networking and Communications
ISSN (Print)2161-9646
ISSN (Electronic)2161-9654

Conference

Conference20th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2024
Country/TerritoryFrance
CityParis
Period10/21/2410/23/24

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Self-Supervised Zero-Shot Noise2Noise Framework for Improved Channel Estimation in RIS-Aided Multi-User Systems'. Together they form a unique fingerprint.

Cite this