Adaptive Handover Management Enables Reliable NextG O-RAN Connectivity in Dense Deployments

Mobile network handovers, the process of seamlessly transferring a connection between cell towers, are becoming increasingly problematic in modern networks due to rising failures and delays, particularly with the advent of dense deployments and higher frequency bands. Michail Kalntis, George Iosifidis, and José Suárez-Varela, from Delft University of Technology and Telefónica Research, alongside Andra Lutu and Fernando A. Kuipers, present a novel framework called CONTRA that tackles these challenges within the emerging O-RAN architecture. This research introduces a method for jointly optimising traditional and conditional handovers, leveraging unique, countrywide mobility data from a major mobile network operator to reveal critical insights into handover performance. By employing a practical algorithm that learns from real-time observations, CONTRA achieves performance comparable to an ideal system with perfect knowledge of future network conditions, significantly improving user throughput and reducing switching costs compared to existing methods and represents a key step towards the flexible, intelligent control envisioned for 6G networks.

Analysis of this extensive dataset, encompassing thousands of cells and data from millions of users, highlighted areas for improvement in both handover strategies, motivating the creation of an adaptive and robust control system for next-generation networks.

By employing a practical algorithm that learns from real-time observations, CONTRA achieves performance comparable to an ideal system with perfect knowledge of future network conditions, significantly improving user throughput and reducing switching costs compared to existing methods. The team studied two variations of CONTRA, one assigning users a specific handover type based on pre-defined requirements, and a dynamic version where the controller intelligently selects the handover type based on current system needs. Extensive evaluations demonstrate that CONTRA consistently outperforms standard handover procedures and reinforcement learning approaches in both simulated and real-world scenarios.

This breakthrough delivers a system specifically designed for near-real-time deployment as an O-RAN application, representing a key step towards the flexible, intelligent control envisioned for 6G networks and proactive, AI-driven mobility management capable of supporting the demanding requirements of future mobile applications and services.

Adaptive Handover Optimization Using Meta-Learning

Scientists have developed CONTRA, a novel framework that jointly optimizes traditional handovers and conditional handovers within the O-RAN architecture, addressing increasing handover failures and delays in modern mobile networks. The research leverages unique, countrywide mobility datasets from a major mobile network operator to gain unprecedented insights into handover performance.

The findings are grounded in the analysis of countrywide mobility datasets, which revealed the need for more adaptive and robust handover control, particularly in dense network deployments and with higher frequency bands. CONTRA incorporates a meta-learning algorithm that adapts to real-time network observations, achieving performance comparable to an ideal system with perfect knowledge of future conditions, demonstrating what is termed “universal no-regret”.

The team studied two variations of CONTRA, one assigning users a specific handover type based on pre-defined requirements, and a dynamic version where the controller intelligently selects the handover type based on current system needs. Extensive evaluations using crowdsourced datasets demonstrate that CONTRA significantly improves user throughput while simultaneously reducing both handover switching costs.

Experiments show CONTRA consistently outperforms both standard handover procedures and reinforcement learning approaches in both dynamic and real-world scenarios, delivering a system specifically designed for near-real-time deployment as an O-RAN application, aligning with the ambitious goals of 6G networks for flexible and intelligent control.

CONTRA Optimises Handover, Boosts Network Performance

This research presents a novel framework, CONTRA, designed to optimise handover procedures in mobile networks, addressing limitations found in both traditional and conditional handover methods. By jointly optimising these techniques within an O-RAN architecture, the team achieved significant improvements in user throughput and reductions in switching costs, demonstrably surpassing the performance of existing systems and reinforcement learning approaches.

The core of CONTRA lies in its ability to adapt to real-time network conditions, achieving performance comparable to an ideal system with perfect knowledge of future network states, without requiring extensive training periods typical of machine learning methods. While the current work focuses on the O-RAN framework, the underlying principles are applicable to other radio access network architectures that support real-time measurements and centralised decision-making.

The authors acknowledge a simplification in their modelling assumptions regarding signal strength, and note that future work could explore more complex representations of the radio environment. Further research directions include extending the framework to accommodate diverse quality of service requirements and investigating its performance in even more challenging network scenarios.