AI-Driven Multi-Modal Adaptive Handover Control Optimization for O-RAN

2026-03-17

AI Summary

This work addresses the challenge of handover optimization in O-RAN, which is hindered by the heterogeneity of user mobility patterns and the dynamic nature of wireless environments. Existing approaches lack mobility awareness and long-term predictive capabilities. To overcome these limitations, the paper proposes a multimodal, mobility-aware hierarchical optimization framework that, for the first time, integrates mobility pattern classification, short-term trajectory and RSRP time-series forecasting, and a PPO-based reinforcement learning policy within a non-real-time RIC rApp. The framework proactively delivers cell-ranking recommendations to xApps via the A1 interface, enabling standards-compliant, low-latency handover decisions when combined with real-time E2 measurements. Experiments using real-world mobility traces demonstrate that the proposed solution significantly reduces ping-pong effects and improves handover reliability compared to the 3GPP A3 event-triggered mechanism and existing machine learning methods.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Handover optimization in O-RAN faces growing challenges due to heterogeneous user mobility patterns and rapidly varying radio conditions. Existing ML-based handover schemes typically operate at the near-RT layer, which lack awareness of the mobility-mode and struggle to incorporate a longer-term predictive context. This paper proposes a multi-modal mobility-aware optimization framework in which all predictive intelligence, including mobility mode classification, short-horizon trajectory and RSRP forecasting, and a PPO Actor--Critic policy, runs entirely inside an rApp in the non-RT RIC. The rApp generates per-UE ranked neighbour-cell recommendations and delivers them to the existing handover xApp through the A1 interface. The xApp combines these rankings with instantaneous E2 measurements and performs the final standards-compliant handover decision. This hierarchical design preserves low-latency execution in the xApp while enabling the rApp to supply richer and mode-specific predictive guidance. Evaluation using mobility traces demonstrates that the proposed approach reduces ping-pong handover events and improves handover reliability compared to conventional 3GPP A3-based and ML-based baselines.

Problem

• Handover optimization
• O-RAN
• Mobility awareness
• Multi-modal prediction
• Radio resource management

Innovation

• multi-modal mobility awareness
• non-RT RIC rApp
• predictive handover optimization
• hierarchical AI control
• O-RAN handover