Name
Abstract | ||
|---|---|---|
This paper presents a vision-aided beam allocation scheme to help conquer the non-trivial issue such as blockage or link failure scenarios of the millimeter wave (mmWave) indoor wireless communication systems. Particularly, a traditional beam allocation scheme degrades the beam training performance due to a non-convex optimization problem, which contain a combinatorial number of local optima and make them extremely challenging for conventional solvers. Hence, we propose a vision-aided beam allocation scheme to overcome the beam optimization issue and enhance the beam training performance in this paper. We employ a camera at the mmWave access point and leverage their scene information to spontaneously sort out the best allocated beam. We also exploit a machine learning tool to predict the allocated mmWave beam from the camera RGB scene. The simulation results show the performance of the proposed vision-aided solutions in terms of beam training and testing performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICTC52510.2021.9621174 | 12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION |
Keywords | DocType | ISSN |
Vision-aided mmWave indoor communications, beam allocation scheme, machine learning, accuracy and loss performance | Conference | 2162-1233 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md. Abdul Latif Sarker | 1 | 0 | 0.34 |
| Igbafe Orikumhi | 2 | 0 | 1.01 |
| Jeongwan Kang | 3 | 0 | 1.01 |
| Hyekyung Jwa | 4 | 0 | 0.34 |
| Jeehyeon Na | 5 | 0 | 0.34 |
| Sunwoo Kim | 6 | 66 | 11.00 |