Name
Title | ||
|---|---|---|
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance |
Abstract | ||
|---|---|---|
We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with $M$ RF chains and $N$ antennas, where $M < N$ . Upon receiving pilot sequences to ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TWC.2021.3052973 | IEEE Transactions on Wireless Communications |
Keywords | DocType | Volume |
Multiuser,precoding,antenna selection,machine learning,neural networks,successive convex optimization | Journal | 20 |
Issue | ISSN | Citations |
6 | 1536-1276 | 2 |
PageRank | References | Authors |
0.37 | 21 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thang X. Vu | 1 | 156 | 16.83 |
| Symeon Chatzinotas | 2 | 1849 | 192.76 |
| Van-Dinh Nguyen | 3 | 179 | 23.75 |
| Dinh Thai Hoang | 4 | 1413 | 77.92 |
| Diep N. Nguyen | 5 | 142 | 26.31 |
| Marco Di Renzo | 6 | 4721 | 269.75 |
| Björn E. Ottersten | 7 | 6418 | 575.28 |