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
- 정종진
- Alternative Author(s)
- 정종진
- Advisor(s)
- 전상운
- Issue Date
- 2020-08
- Publisher
- 한양대학교
- Degree
- Master
- 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://repository.hanyang.ac.kr/handle/20.500.11754/153202http://hanyang.dcollection.net/common/orgView/200000438221
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING(전자공학과) > Theses (Master)
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