Joint Mobile IAB Node Positioning and Scheduler Selection in Locations With Substantial Obstacles

TL;DR

This paper proposes a genetic algorithm-based method for optimal positioning and scheduling of mobile IAB nodes in obstacle-rich environments, significantly enhancing network capacity and resilience during emergencies.

Contribution

It introduces a novel joint optimization approach for MIAB placement and scheduler selection considering obstacles, user associations, and capacity constraints.

Findings

Capacity improvements up to 200% for the 90th percentile.

Enhanced network resilience during emergency capacity demands.

Effective handling of obstacles and user density in MIAB positioning.

Abstract

Integrated Access and Backhaul (IAB) in cellular networks combines access and backhaul within a wireless infrastructure reducing reliance on fibre-based backhaul. This enables faster and more cost-effective network expansion, especially in hard-to-reach areas. Positioning a mobile IAB node (MIAB) in a seaport environment, in order to ensure ondemand, resilient wireless connectivity, presents unique challenges due to the high density of User Equipments (UEs) and potential shadowing effects caused by obstacles. This paper addresses the problem of positioning MIABs within areas containing UEs, IAB donors (CFs), and obstacles. Our approach considers user associations and different types of scheduling, ensuring MIABs and CFs meet the capacity requirements of a special team of served UEs, while not exceeding backhaul capacity. Using Genetic Algorithm (GA) solver, we achieve capacity improvement gains, by up to 200% for the 90th percentile. The proposed solution improves network performance, particularly during emergency capacity demands.