Channel Hardening of IRS-Aided Multi-Antenna Systems: How Should IRSs Scale?

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Ali Bereyhi - , Friedrich-Alexander University Erlangen-Nürnberg, University of Toronto (Author)
  • Saba Asaad - , Friedrich-Alexander University Erlangen-Nürnberg, York University Toronto (Author)
  • Chongjun Ouyang - , Beijing University of Posts and Telecommunications (Author)
  • Ralf R. Muller - , Friedrich-Alexander University Erlangen-Nürnberg (Author)
  • Rafael F. Schaefer - , Chair of Information Theory and Machine Learning (Author)
  • H. Vincent Poor - , Princeton University (Author)

Abstract

It is widely believed that large IRS-aided MIMO settings maintain the fundamental features of massive MIMO systems. This work gives a rigorous proof that confirms this belief. We show that using a large passive IRS, the end-to-end MIMO channel between the transmitter and the receiver always hardens, even if the IRS elements are strongly correlated. For fading direct and reflection links between the transmitter and the receiver, our derivations demonstrate that for a large number of reflecting elements on the IRS, the capacity of the end-to-end channel is accurately approximated by a real-valued Gaussian random variable whose variance goes to zero as the number of IRS elements grows unboundedly large. The order of this drop depends on how the physical dimensions of the IRS grow. We derive this order explicitly. Numerical experiments show that the closed-form approximation very closely matches the histogram of the capacity term, even in practical scenarios. As a sample application of the results, we characterize the dimensional trade-off between the transmitter and the IRS. The result is intuitive: For a target performance, the larger the IRS is, the fewer transmit antennas are required.

Details

Original languageEnglish
Pages (from-to)2321-2335
Number of pages15
JournalIEEE journal on selected areas in communications
Volume41
Issue number8
Publication statusPublished - 1 Aug 2023
Peer-reviewedYes

External IDs

ORCID /0000-0002-1702-9075/work/165878284

Keywords

Keywords

  • asymptotic channel hardening, ergodic capacity, large-system analysis, outage probability, Reconfigurable intelligent surface