Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN

2025-09-05

AI Summary

6G-integrated access and backhaul (IAB) networks face a fundamental challenge in jointly optimizing energy efficiency and throughput due to multi-hop backhaul constraints. To address this, we propose a bi-objective optimization framework unifying routing and resource allocation. First, we develop a capacity model that explicitly couples transmit power levels with achievable data rates—overcoming limitations of conventional static modeling. Second, we design a Pareto-optimal algorithm that simultaneously minimizes energy consumption and maximizes network throughput. Third, we embed the optimization within an Open RAN-based closed-loop control architecture to enable dynamic, scalable, and real-time decision-making. Evaluated via FR3-band simulations driven by two months of real-world traffic traces from a Milan operator, our approach reduces the number of active IAB nodes by ~32%, yielding substantial energy savings, while guaranteeing a minimum user rate of 100 Mbps during peak hours. Results demonstrate both practical viability and state-of-the-art performance for large-scale 6G IAB deployments.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

As networks evolve towards 6G, Mobile Network Operators (MNOs) must accommodate diverse requirements and at the same time manage rising energy consumption. Integrated Access and Backhaul (IAB) networks facilitate dense cellular deployments with reduced infrastructure complexity. However, the multi-hop wireless backhauling in IAB networks necessitates proper routing and resource allocation decisions to meet the performance requirements. At the same time, cell densification makes energy optimization crucial. This paper addresses the joint optimization of routing and resource allocation in IAB networks through two distinct objectives: energy minimization and throughput maximization. We develop a novel capacity model that links power levels to achievable data rates. We propose two practical large-scale approaches to solve the optimization problems and leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to integrate the solutions. The approaches are evaluated on diverse scenarios built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed approaches effectively reduces number of activated nodes to save energy and achieves approximately 100 Mbps of minimum data rate per User Equipment (UE) during peak hours of the day using spectrum within the Frequency Range (FR) 3, or upper midband. The results validate the practical applicability of our framework for next-generation IAB network deployment and optimization.

Problem

• Joint optimization of routing and resource allocation
• Energy minimization and throughput maximization in IAB networks
• Practical large-scale approaches for Open RAN integration

Innovation

• Joint routing and resource allocation optimization for IAB networks
• Novel capacity model linking power levels to data rates
• Open RAN closed-loop control framework integration