Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity
5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercussions in the signal degradation in urban scenarios makes it a challenge to maintain stable coverage and connectivity...
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Zusammenfassung: | 5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's
existent maximum data rates. However, the effective size of 5G antennas and
consequently its repercussions in the signal degradation in urban scenarios
makes it a challenge to maintain stable coverage and connectivity. In order to
obtain the best from both technologies, recent dual connectivity solutions have
proved their capabilities to improve performance when compared with coexistent
standalone 5G and 4G technologies. Reinforcement learning (RL) has shown its
huge potential in wireless scenarios where parameter learning is required given
the dynamic nature of such context. In this paper, we propose two reinforcement
learning algorithms: a single agent RL algorithm named Clipped Double
Q-Learning (CDQL) and a hierarchical Deep Q-Learning (HiDQL) to improve
Multiple Radio Access Technology (multi-RAT) dual-connectivity handover. We
compare our proposal with two baselines: a fixed parameter and a dynamic
parameter solution. Simulation results reveal significant improvements in terms
of latency with a gain of 47.6% and 26.1% for Digital-Analog beamforming (BF),
17.1% and 21.6% for Hybrid-Analog BF, and 24.7% and 39% for Analog-Analog BF
when comparing the RL-schemes HiDQL and CDQL with the with the existent
solutions, HiDQL presented a slower convergence time, however obtained a more
optimal solution than CDQL. Additionally, we foresee the advantages of
utilizing context-information as geo-location of the UEs to reduce the beam
exploration sector, and thus improving further multi-RAT handover latency
results. |
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DOI: | 10.48550/arxiv.2301.05391 |