English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE'sexistent maximum data rates. However, the effective size of 5G antennas andconsequently its repercussions in the signal degradation in urban scenariosmakes it a challenge to maintain stable coverage and connectivity. In order toobtain the best from both technologies, recent dual connectivity solutions haveproved their capabilities to improve performance when compared with coexistentstandalone 5G and 4G technologies. Reinforcement learning (RL) has shown itshuge potential in wireless scenarios where parameter learning is required giventhe dynamic nature of such context. In this paper, we propose two reinforcementlearning algorithms: a single agent RL algorithm named Clipped DoubleQ-Learning (CDQL) and a hierarchical Deep Q-Learning (HiDQL) to improveMultiple Radio Access Technology (multi-RAT) dual-connectivity handover. Wecompare our proposal with two baselines: a fixed parameter and a dynamicparameter solution. Simulation results reveal significant improvements in termsof 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 BFwhen comparing the RL-schemes HiDQL and CDQL with the with the existentsolutions, HiDQL presented a slower convergence time, however obtained a moreoptimal solution than CDQL. Additionally, we foresee the advantages ofutilizing context-information as geo-location of the UEs to reduce the beamexploration sector, and thus improving further multi-RAT handover latencyresults.

Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity