Table of Contents
Fetching ...

Load Balancing-based Topology Adaptation for Integrated Access and Backhaul Networks

Raul Victor de O. Paiva, Fco. Italo G. Carvalho, Fco. Rafael M. Lima, Victor F. Monteiro, Diego A. Sousa, Darlan C. Moreira, Tarcisio F. Maciel, Behrooz Makki

TL;DR

This paper addresses topology adaptation in mobile IAB networks by moving beyond traditional RSRP-only criteria to incorporate traffic load in donor nodes. It proposes a load-balancing TA algorithm that selects IAB donors based on both backhaul link quality and current donor load (e.g., buffered bits), aiming to prevent overloads and improve passenger user experience. Through simulations, the approach shows notable improvements in uplink throughput for passengers and more modest gains in downlink, with minimal degradation to pedestrian QoS. The work advances practical IAB deployment by enhancing fairness and resilience of backhaul in dynamic, mobility-driven network scenarios.

Abstract

Integrated access and backhaul (IAB) technology is a flexible solution for network densification. IAB nodes can also be deployed in moving nodes such as buses and trains, i.e., mobile IAB (mIAB). As mIAB nodes can move around the coverage area, the connection between mIAB nodes and their parent macro base stations (BSs), IAB donor, is sometimes required to change in order to keep an acceptable backhaul link, the so called topology adaptation (TA). The change from one IAB donor to another may strongly impact the system load distribution, possibly causing unsatisfactory backhaul service due to the lack of radio resources. Based on this, TA should consider both backhaul link quality and traffic load. In this work, we propose a load balancing algorithm based on TA for IAB networks, and compare it with an approach in which TA is triggered based on reference signal received power (RSRP) only. The results show that our proposed algorithm improves the passengers worst connections throughput in uplink (UL) and, more modestly, also in downlink (DL), without impairing the pedestrian quality of service (QoS) significantly.

Load Balancing-based Topology Adaptation for Integrated Access and Backhaul Networks

TL;DR

This paper addresses topology adaptation in mobile IAB networks by moving beyond traditional RSRP-only criteria to incorporate traffic load in donor nodes. It proposes a load-balancing TA algorithm that selects IAB donors based on both backhaul link quality and current donor load (e.g., buffered bits), aiming to prevent overloads and improve passenger user experience. Through simulations, the approach shows notable improvements in uplink throughput for passengers and more modest gains in downlink, with minimal degradation to pedestrian QoS. The work advances practical IAB deployment by enhancing fairness and resilience of backhaul in dynamic, mobility-driven network scenarios.

Abstract

Integrated access and backhaul (IAB) technology is a flexible solution for network densification. IAB nodes can also be deployed in moving nodes such as buses and trains, i.e., mobile IAB (mIAB). As mIAB nodes can move around the coverage area, the connection between mIAB nodes and their parent macro base stations (BSs), IAB donor, is sometimes required to change in order to keep an acceptable backhaul link, the so called topology adaptation (TA). The change from one IAB donor to another may strongly impact the system load distribution, possibly causing unsatisfactory backhaul service due to the lack of radio resources. Based on this, TA should consider both backhaul link quality and traffic load. In this work, we propose a load balancing algorithm based on TA for IAB networks, and compare it with an approach in which TA is triggered based on reference signal received power (RSRP) only. The results show that our proposed algorithm improves the passengers worst connections throughput in uplink (UL) and, more modestly, also in downlink (DL), without impairing the pedestrian quality of service (QoS) significantly.
Paper Structure (1 section)

This paper contains 1 section.

Table of Contents

  1. Introduction