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

TL;DR

This paper introduces a novel optimization model for joint routing and energy efficiency in IAB networks, leveraging O-RAN control frameworks to significantly reduce energy consumption while maintaining user capacity.

Contribution

It presents a new binary linear programming approach for energy optimization in IAB networks, utilizing real-world traffic data for validation.

Findings

Reduces RAN energy consumption by 47%.

Ensures minimum capacity for each user equipment.

Validates approach with real city traffic data.

Abstract

Energy consumption represents a major part of the operating expenses of mobile network operators. With the densification foreseen with 5G and beyond, energy optimization has become a problem of crucial importance. While energy optimization is widely studied in the literature, there are limited insights and algorithms for energy-saving techniques for Integrated Access and Backhaul (IAB), a self-backhauling architecture that ease deployment of dense cellular networks reducing the number of fiber drops. This paper proposes a novel optimization model for dynamic joint routing and energy optimization in IAB networks. We leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to minimize the number of active IAB nodes while maintaining a minimum capacity per User Equipment (UE). The proposed approach formulates the problem as a binary nonlinear program, which is transformed into an equivalent binary linear program and solved using the Gurobi solver. The approach is evaluated on a scenario built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed optimization model reduces the RAN energy consumption by 47%, while guaranteeing a minimum capacity for each UE.