TY - GEN
T1 - A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems
AU - Ma, Yifan
AU - Shen, Yifei
AU - Yu, Xianghao
AU - Zhang, Jun
AU - Song, S. H.
AU - Letaief, Khaled B.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environment. To fully exploit the potential of IRS-assisted wireless systems, reflective elements have to be jointly optimized with conventional communication techniques. However, the resulting optimization problems pose significant algorithmic challenges, mainly due to the large-scale non-convex constraints induced by the passive hardware implementations. In this paper, we propose a low-complexity algorithmic framework incorporating alternating optimization and gradient-based methods for largescale IRS-assisted wireless systems. The proposed algorithm provably converges to a stationary point of the optimization problem. Extensive simulation results demonstrate that the proposed framework provides significant speedups compared with existing algorithms, while achieving a comparable or better performance.
AB - Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environment. To fully exploit the potential of IRS-assisted wireless systems, reflective elements have to be jointly optimized with conventional communication techniques. However, the resulting optimization problems pose significant algorithmic challenges, mainly due to the large-scale non-convex constraints induced by the passive hardware implementations. In this paper, we propose a low-complexity algorithmic framework incorporating alternating optimization and gradient-based methods for largescale IRS-assisted wireless systems. The proposed algorithm provably converges to a stationary point of the optimization problem. Extensive simulation results demonstrate that the proposed framework provides significant speedups compared with existing algorithms, while achieving a comparable or better performance.
UR - http://www.scopus.com/inward/record.url?scp=85102927741&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps50303.2020.9367432
DO - 10.1109/GCWkshps50303.2020.9367432
M3 - Conference article published in proceeding or book
AN - SCOPUS:85102927741
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020
Y2 - 7 December 2020 through 11 December 2020
ER -