Name
Abstract | ||
|---|---|---|
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 large-scale 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GCWkshps50303.2020.9367432 | 2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS) |
DocType | ISSN | Citations |
Conference | 2166-0069 | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Ma | 1 | 0 | 0.34 |
| Yifei Shen | 2 | 2 | 1.06 |
| Xianghao Yu | 3 | 256 | 9.23 |
| Jun Zhang | 4 | 3772 | 190.36 |
| S. H. Song | 5 | 107 | 8.51 |
| Khaled B. Letaief | 6 | 7 | 1.47 |