Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 전상운 | - |
dc.contributor.author | 정종진 | - |
dc.date.accessioned | 2020-08-28T16:59:36Z | - |
dc.date.available | 2020-08-28T16:59:36Z | - |
dc.date.issued | 2020-08 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/153202 | - |
dc.identifier.uri | http://hanyang.dcollection.net/common/orgView/200000438221 | en_US |
dc.description.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. | - |
dc.publisher | 한양대학교 | - |
dc.title | Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems | - |
dc.type | Theses | - |
dc.contributor.googleauthor | Jeong, Jong Jin | - |
dc.contributor.alternativeauthor | 정종진 | - |
dc.sector.campus | S | - |
dc.sector.daehak | 대학원 | - |
dc.sector.department | 전자공학과 | - |
dc.description.degree | Master | - |
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