Intelligent Backhaul Link Selection for Traffic Offloading in B5G Networks
IEEE Access, vol. 12, pp. 106757–106769
Abstract
Fifth Generation (5G) mobile networks considers an expansive set of heterogeneous services with stringent Quality of Service (QoS) requirements, and traffic demand with inherent spatial-temporal distribution, which places the backhaul network deployment under potential strain. In this paper, we propose to harness network slicing, Integrated Access and Backhaul (IAB) technology coupled with satellite connectivity to build a dynamic wireless backhaul network that can provide additional backhaul capacity to the base stations on demand when the wired backhaul link is temporarily out of capacity. To construct the network design, Deep Reinforcement Learning (DRL) models are used to select, for each network slice of the congested base station, an appropriate backhaul link from the pool of available IAB and satellite links that meets the QoS requirements (i.e., throughput and latency) of the slice. Simulation results show that around 20 episodes are sufficient to train a Double Deep Q-Network (DDQN) agent, with one fully-connected hidden layer and Rectified Linear Unit (ReLU) activation function, that adjusts the topology of the backhaul network.
Authors 5
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Affiliation as printed
Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, U.K
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Affiliation as printed
Chair for Distributed Signal Processing, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Information Technologies Group, atlanTTic Research Center, University of Vigo, Vigo, Spain
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Affiliation as printed
Information Technologies Group, atlanTTic Research Center, University of Vigo, Vigo, Spain
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Affiliation as printed
Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, U.K
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