A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols in Mobile Networks
CoRR(2024)
摘要
Due to an ever-increasing number of participants and new areas of
application, the demands on mobile communications systems are continually
increasing. In order to deliver higher data rates, enable mobility and
guarantee QoS requirements of subscribers, these systems and the protocols used
are becoming more complex. By using higher frequency spectrums, cells become
smaller and more base stations have to be deployed. This leads to an increased
number of handovers of user equipments between base stations in order to enable
mobility, resulting in potentially more frequent radio link failures and rate
reduction. The persistent switching between the same base stations, commonly
referred to as "ping-pong", leads to a consistent reduction of data rates. In
this work, we propose a method for handover optimization by using proximal
policy optimization in mobile communications to learn an adaptive handover
protocol. The resulting agent is highly flexible regarding different travelling
speeds of user equipments, while outperforming the standard 5G NR handover
protocol by 3GPP in terms of average data rate and number of radio link
failures. Furthermore, the design of the proposed environment demonstrates
remarkable accuracy, ensuring a fair comparison with the standard 3GPP
protocol.
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