Machine Learning Based Beam Tracking in mmWave Systems
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URL
Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
Master's thesis
Authors
Date
2021-03-15
Department
Major/Subject
Autonomous Systems
Mcode
ELEC3055
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
40
Series
Abstract
The demand for high data rates communication and scarcity of spectrum in existing microwave bands has been the key aspect in 5G. To fulfill these demands, the millimeter wave (mmWave) with large bandwidths has been proposed to enhance the efficiency and the stability of the 5G network. In mmWave communication, the concentration of the transmission signal from the antenna is conducted by beamforming and beam tracking. However, state-of-art methods in beam tracking lead to high resource consumption. To address this problem, we develop a machine-learning-based solution for overhead reduction. In this paper, a scenario configuration simulator is proposed as the data collection approach. Several LSTM based time series prediction models are trained for experiments. Since the overhead is reduced by decreasing the number of sweeping beams in this solution, two data imputation methods are proposed based on Bayesian ridge regression and Pearson correlation coefficient. Both qualitative and quantitative experimental results on several kinds of datasets demonstrate the efficacy of our solution.Description
Supervisor
Fischione, CarloThesis advisor
Wang, YuKeywords
meam, mmWave, 5G, machine-learning