TY - GEN
T1 - Physics-based Modeling of Large Intelligent Reflecting Surfaces for Scalable Optimization
AU - Najafi, Marzieh
AU - Jamali, Vahid
AU - Schober, Robert
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - In this paper, we develop a physics-based model that allows a scalable optimization of large intelligent reflecting surfaces (IRSs). The basic idea is to partition the IRS unit cells into several subsets, referred to as tiles, and model the impact of each tile on the wireless channel. Borrowing concepts from the radar literature, we model each tile as an anomalous reflector, and derive its impact on the wireless channel for given unit cell phase shifts by solving the corresponding integral equations for the electric and magnetic vector fields. Based on this model, one can design the phase shifts of the unit cells of a tile offline for the support of several transmission modes and then select the best mode online for a given channel realization. Therefore, the number of tiles and transmission modes in the proposed model are design parameters that can be adjusted to trade performance for complexity.
AB - In this paper, we develop a physics-based model that allows a scalable optimization of large intelligent reflecting surfaces (IRSs). The basic idea is to partition the IRS unit cells into several subsets, referred to as tiles, and model the impact of each tile on the wireless channel. Borrowing concepts from the radar literature, we model each tile as an anomalous reflector, and derive its impact on the wireless channel for given unit cell phase shifts by solving the corresponding integral equations for the electric and magnetic vector fields. Based on this model, one can design the phase shifts of the unit cells of a tile offline for the support of several transmission modes and then select the best mode online for a given channel realization. Therefore, the number of tiles and transmission modes in the proposed model are design parameters that can be adjusted to trade performance for complexity.
UR - http://www.scopus.com/inward/record.url?scp=85094993625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85094993625&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443337
DO - 10.1109/IEEECONF51394.2020.9443337
M3 - Conference contribution
AN - SCOPUS:85094993625
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 559
EP - 563
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -