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Machine Learning based RATs Selection supporting Multi-Connectivity for Reliability

Lee, Haeyoung, Vahid, Seiamak and Moessner, Klaus (2019) Machine Learning based RATs Selection supporting Multi-Connectivity for Reliability In: 14th EAI International Conference on Cognitive Radio Oriented Wireless Networks (CrownCOM 2019), 11-12 Jun 2019, Poznan, Poland.

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Abstract

While ultra-reliable and low latency communication (uRLLC) is expected to cater to emerging services requiring real-time control, such as factory automation and autonomous driving, the design of uRLLC of stringent requirements would be very challenging. Among novel solutions to satisfy uRLLC's requirements, interface diversity is widely regarded as an efficient enabler of ultra-reliable connectivity. When mobile de- vices are connected to multiple base stations (BSs) of different radio access technologies (RATs) and same data is transmitted via multiple links simultaneously, the transmission reliability can be improved. How- ever, duplicate transmission of same data causes an increase in the traffic loads, leading to radio resource shortage. Considering it, efficient config- uration of multi-connectivity (MC) for mobile devices is important. In this paper, the RAT selection scheme including efficient MC configura- tion is proposed. By adopting distributed reinforcement learning (RL), each device could learn the policy for efficient MC configuration and select appropriate RATs. Simulation results show that 20.8% reliabil- ity improvements over the single connectivity scheme is observed. Com- paring to the method to configure MC for devices all the time, 37.6% improvement is achieved at high traffic loads.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Lee, Haeyounghaeyoung.lee@surrey.ac.uk
Vahid, SeiamakS.Vahid@surrey.ac.uk
Moessner, KlausK.Moessner@surrey.ac.uk
Date : 2019
Funders : European Union's Horizon 2020
Copyright Disclaimer : © 2019 the authors
Uncontrolled Keywords : RAT selection; Multi-connectivity; Machine Learning; URLLC
Related URLs :
Depositing User : Clive Harris
Date Deposited : 13 May 2019 13:38
Last Modified : 11 Jun 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/851800

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