Vision-Aided Beam Allocation for Indoor mmWave Communications
- Authors
- Sarker, Md. Abdul Latif; Orikumhi, Igbafe; Kang, Jeongwan; Jwa, Hye-Kyung; Na, Jee-Hyeon; Kim, Sunwoo
- Issue Date
- Dec-2021
- Publisher
- IEEE Computer Society
- Keywords
- accuracy and loss performance; beam allocation scheme; machine learning; Vision-aided mmWave indoor communications
- Citation
- International Conference on ICT Convergence, v.2021, no.October, pp.1403 - 1408
- Indexed
- SCOPUS
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2021
- Number
- October
- Start Page
- 1403
- End Page
- 1408
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140084
- DOI
- 10.1109/ICTC52510.2021.9621174
- ISSN
- 2162-1233
- 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.
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