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

2024-09-25

AI Summary

In dense urban environments with high obstructions—such as seaports—mobile integrated access and backhaul (MIAB) nodes face severe challenges in 3D localization, limited fronthaul/backhaul capacity, and difficulty meeting heterogeneous capacity requirements across user groups. To address these issues, this paper proposes a capacity-constrained joint optimization framework for MIAB networks. It is the first to jointly model 3D spatial deployment of MIAB nodes and scheduler selection, while explicitly incorporating user equipment (UE) association, coordinated multipoint (CoMP)-enabled fronthaul/backhaul links, and obstruction-induced shadowing effects. A genetic algorithm is employed to solve the coupled problems of user association, resource scheduling, and node placement. Experimental results demonstrate a 200% improvement in the 90th-percentile per-user capacity, significantly enhancing network robustness, spectral efficiency, and scalability—thereby effectively supporting ultra-dense terminal deployments and mission-critical emergency communications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Positioning mobile IAB nodes in obstacle-rich environments
• Optimizing user associations and scheduling for capacity
• Improving network performance during emergency demands

Innovation

• Genetic Algorithm for MIAB positioning
• Enhanced network capacity up to 200%
• Resilient scheduling in obstacle-rich areas