On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach

TL;DR

This paper proposes genetic algorithm-based methods for optimizing topology and routing in 5G integrated access and backhaul networks, analyzing their impact on coverage, blockage mitigation, and network densification.

Contribution

It introduces novel genetic algorithm schemes for IAB node placement and backhaul link distribution, addressing routing challenges and system performance in millimeter wave 5G networks.

Findings

Genetic algorithms improve IAB network topology and routing efficiency.

Proper topology enhances coverage probability and mitigates blockages.

Analysis of parameters like antenna gain and foliage impacts system performance.

Abstract

In this paper, we study the problem of topology optimization and routing in integrated access and backhaul (IAB) networks, as one of the promising techniques for evolving 5G networks. We study the problem from different perspectives. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution, and evaluate the effect of routing on bypassing temporal blockages. Here, concentrating on millimeter wave-based communications, we study the service coverage probability, defined as the probability of the event that the user equipments' (UEs) minimum rate requirements are satisfied. Moreover, we study the effect of different parameters such as the antenna gain, blockage and tree foliage on the system performance. Finally, we summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on routing in IAB networks, and discuss the main challenges for enabling mesh-based IAB networks. As we show, with a proper network topology, IAB is an attractive approach to enable the network densification required by 5G and beyond.