Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems
- Authors
- Jeong, Jongjin; Lim, Sunghoon; Song, Yujae; Jeon, Sangwoon
- Issue Date
- Jul-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- beam tracking; massive MIMO; mmWave; Reinforcement learning
- Citation
- Proceedings - IEEE INFOCOM, v.2020-July, pp.764 - 773
- Indexed
- SCIE
SCOPUS
- Journal Title
- Proceedings - IEEE INFOCOM
- Volume
- 2020-July
- Start Page
- 764
- End Page
- 773
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1822
- DOI
- 10.1109/INFOCOM41043.2020.9155475
- ISSN
- 0743-166X
- 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. © 2020 IEEE.
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