Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems
- Title
- Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems
- Author
- 전상운
- Issue Date
- 2020-07
- Publisher
- IEEE
- Citation
- IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, Page. 764-773
- Abstract
- In this paper, we consider a joint beam tracking and pattern optimization problem for massive multiple input multiple output (MIMO) systems in which the base station (BS) selects a beamforming codebook and performs adaptive beam tracking taking into account the user mobility. A joint adaptation scheme is developed in a two-phase reinforcement learning framework which utilizes practical signaling and feedback information. In particular, an inner agent adjusts the transmission beam index for a given beamforming codebook based on short-term instantaneous signal-to-noise ratio (SNR) rewards. In addition, an outer agent selects the beamforming codebook based on long-term SNR rewards. Simulation results demonstrate that the proposed online learning outperforms conventional codebook-based beamforming schemes using the same number of feedback information. It is further shown that joint beam tracking and beam pattern adaptation provides a significant SNR gain compared to the beam tracking only schemes, especially as the user mobility increases.
- URI
- https://ieeexplore.ieee.org/document/9155475?arnumber=9155475&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/164855
- ISBN
- 978-1-7281-6412-0
- ISSN
- 2641-9874
- DOI
- 10.1109/INFOCOM41043.2020.9155475
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MILITARY INFORMATION ENGINEERING(국방정보공학과) > Articles
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