Intelligent Backhaul Link Selection for Traffic Offloading in B5G Networks

TL;DR

This paper proposes a dynamic backhaul link selection method using deep reinforcement learning to optimize traffic offloading in 5G networks with heterogeneous services and stringent QoS demands.

Contribution

It introduces a DRL-based approach for intelligent backhaul link selection leveraging network slicing, IAB, and satellite links to enhance capacity and meet QoS in B5G networks.

Findings

Training converges in about 20 episodes with DDQN.

The proposed method effectively adjusts backhaul topology.

Simulation results demonstrate improved traffic offloading.

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.