How Should IRSs Scale to Harden Multi-Antenna Channels?

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Ali Bereyhi - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Saba Asaad - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Chongjun Ouyang - , Beijing University of Posts and Telecommunications (Autor:in)
  • Ralf R. Muller - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Rafael F. Schaefer - , Universität Siegen (Autor:in)
  • H. Vincent Poor - , Princeton University (Autor:in)

Abstract

This work extends the concept of channel hardening to multi-antenna systems that are aided by intelligent reflecting surfaces (IRSs). For fading links between a multi-antenna transmitter and a single-antenna receiver, we derive an accurate approximation for the distribution of the input-output mutual information when the number of reflecting elements grows large. The asymptotic results demonstrate that by increasing the number of elements on the IRS, the end-to-end channel hardens as long as the physical dimensions of the IRS grow as well. The growth rate however need not to be of a specific order and can be significantly sub-linear. The validity of the analytical result is confirmed by numerical experiments.

Details

OriginalspracheEnglisch
Titel2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
Herausgeber (Verlag)IEEE Computer Society
Seiten276-280
Seitenumfang5
ISBN (elektronisch)978-1-6654-0633-8
PublikationsstatusVeröffentlicht - 2022
Peer-Review-StatusJa
Extern publiziertJa

Publikationsreihe

ReiheSensor Array and Multichannel Signal Processing Workshop
Band2022-June

Konferenz

Titel12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
Dauer20 - 23 Juni 2022
StadtTrondheim
LandNorwegen

Externe IDs

ORCID /0000-0002-1702-9075/work/165878345

Schlagworte

Schlagwörter

  • channel hardening, Intelligent reflecting surfaces, large-system analysis