Performance Tradeoff of RIS Beam Training: Overhead vs. Achievable SNR

Laue F, Garkisch M, Jamali V, Schober R (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Conference Record of the Asilomar Conference on Signals, Systems and Computers Institute of Electrical and Electronics Engineers

Publisher: IEEE Computer Society

Book Volume: 2022-October

Pages Range: 408-412

Conference Proceedings Title: Conference Record - Asilomar Conference on Signals, Systems and Computers

Event location: Virtual, Online

ISBN: 9781665459068

DOI: 10.1109/IEEECONF56349.2022.10052056

Abstract

In this paper, we consider large reconfigurable intelligent surfaces (RISs) for millimeter-wave (mmWave) communication systems and study the tradeoff between the overhead of RIS beam training and the achievable signal-to-noise ratio (SNR). More specifically, we analyze the overhead of codebook-based RIS configuration for three popular training strategies. Furthermore, we derive scaling laws for the achievable SNR and highlight their differences for small and large RIS codebooks. Our numerical simulations show that low-overhead training strategies are essential to maintain high system performance.

Authors with CRIS profile

Friedemann Laue Lehrstuhl für Digitale Übertragung (IDC) Moritz Garkisch Lehrstuhl für Digitale Übertragung (IDC) Robert Schober Lehrstuhl für Digitale Übertragung (IDC)

How to cite

APA:

Laue, F., Garkisch, M., Jamali, V., & Schober, R. (2022). Performance Tradeoff of RIS Beam Training: Overhead vs. Achievable SNR. In Michael B. Matthews (Eds.), Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 408-412). Virtual, Online, US: IEEE Computer Society.

MLA:

Laue, Friedemann, et al. "Performance Tradeoff of RIS Beam Training: Overhead vs. Achievable SNR." Proceedings of the 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022, Virtual, Online Ed. Michael B. Matthews, IEEE Computer Society, 2022. 408-412.

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