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(s)
정종진; 송유재; 임성훈; 전상운
Alternative Author(s)
송유재
Publication Year
2020-07
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://sciwatch.kiost.ac.kr/handle/2020.kiost/21006
Bibliographic Citation
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020
Publisher
IEEE
Type
Conference
Language
English
Publisher
IEEE
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