[Paper] Meta-Learning-Based Handover Management in NextG O-RAN

Source: arXiv - 2512.22022v1

Overview

The paper introduces CONTRA, a meta‑learning‑driven framework that simultaneously decides between traditional handovers (THOs) and the newer conditional handovers (CHOs) inside an O‑RAN‑based 5G/6G network. By leveraging real‑world mobility traces from a leading operator, the authors show how adaptive, data‑driven handover control can boost throughput and cut signaling overhead in dense, high‑frequency deployments.

Key Contributions

• Country‑wide mobility dataset: First public release of large‑scale handover logs from a top‑tier MNO, exposing the real‑world trade‑offs between THOs and CHOs.
• CONTRA framework: A unified O‑RAN xApp that jointly optimizes THO and CHO decisions, supporting both static (pre‑assigned) and dynamic (on‑the‑fly) selection of handover type.
• Meta‑learning algorithm: A practical, fast‑adapting meta‑learner that achieves universal no‑regret performance—i.e., it behaves almost as well as an oracle with perfect future knowledge.
• Near‑real‑time deployment: Designed for the O‑RAN near‑real‑time RAN Intelligent Controller (RIC), enabling plug‑and‑play integration with existing 5G stacks.
• Extensive evaluation: Benchmarked against 3GPP‑compliant heuristics and state‑of‑the‑art reinforcement‑learning baselines using crowdsourced datasets, demonstrating measurable gains in user throughput and reduced handover switching costs.

Methodology

1. Data collection & preprocessing – The authors gathered anonymized handover events (signal strength, UE speed, cell load, etc.) across an entire country and transformed them into a time‑series feature set suitable for online learning.
2. Problem formulation – Handover control is cast as a sequential decision problem: at each decision epoch the controller chooses (i) whether to trigger a handover, (ii) which target cell, and (iii) which handover type (THO vs. CHO). Two variants are studied:
   • Static assignment: each UE is pre‑tagged with a preferred handover type (e.g., latency‑critical vs. throughput‑heavy services).
   • Dynamic assignment: the controller can switch the handover type per decision based on current network state.
3. Meta‑learning core – CONTRA employs a model‑agnostic meta‑learning (MAML) style approach: a meta‑policy is trained across many simulated episodes to learn a good initialization, then quickly fine‑tuned online using the latest observations from each UE. This yields rapid adaptation to changing radio conditions without the long burn‑in typical of pure RL.
4. Integration with O‑RAN – The meta‑learner runs as an xApp on the near‑real‑time RIC, receiving KPI streams (e.g., RSRP, load) via the O‑RAN E2 interface and pushing handover commands back to the distributed units (DUs).
5. Evaluation pipeline – Real‑world traces are replayed in a high‑fidelity network simulator that respects O‑RAN timing constraints. Metrics include average user throughput, handover success rate, and the switching cost (signaling overhead incurred when changing handover type).

Results & Findings

• Dynamic CONTRA consistently outperforms the static version, confirming the value of on‑the‑fly handover‑type selection.
• The meta‑learner reaches near‑oracle performance after only a few minutes of live data, far faster than conventional RL which needs hours of exploration.
• In high‑mobility scenarios (e.g., users on trains), CHOs become advantageous; CONTRA automatically shifts to CHOs, while in dense urban hotspots it prefers THOs to reduce reservation overhead.

Practical Implications

• Operator cost savings – By cutting unnecessary signaling and improving handover success, operators can lower back‑haul load and reduce the need for over‑provisioned radio resources.
• Better QoE for diverse services – Latency‑sensitive applications (AR/VR, autonomous driving) can be steered toward CHOs, while bulk‑download or video streaming can stay with THOs, delivering service‑aware performance.
• Plug‑and‑play O‑RAN integration – Since CONTRA is an xApp, vendors can embed it into existing near‑real‑time RIC deployments without hardware changes, aligning with the open‑source O‑RAN ecosystem.
• Foundation for 6G intelligent control – The meta‑learning paradigm demonstrates how future networks can continuously adapt to new spectrum bands, ultra‑dense small cells, and evolving traffic patterns with minimal manual tuning.
• Data‑driven policy updates – Operators can periodically retrain the meta‑policy on fresh mobility logs, ensuring the handover logic stays current as city layouts or user behavior evolve.

Limitations & Future Work

• Dataset scope – Although country‑wide, the data originates from a single operator and a specific frequency band; cross‑operator or multi‑band validation is needed.
• Model complexity vs. RIC constraints – The meta‑learner’s compute footprint, while modest, may still challenge low‑power edge RIC deployments; future work could explore model compression or federated updates.
• Security & privacy – Real‑time ingestion of UE measurements raises concerns; integrating privacy‑preserving mechanisms (e.g., differential privacy) is an open avenue.
• Extension to other RAN functions – The authors suggest applying the same meta‑learning engine to scheduling, beam management, or slice orchestration—promising directions for a truly holistic intelligent RAN.

Authors

• Michail Kalntis
• George Iosifidis
• José Suárez-Varela
• Andra Lutu
• Fernando A. Kuipers

Paper Information

• arXiv ID: 2512.22022v1
• Categories: cs.NI, cs.AI
• Published: December 26, 2025
• PDF: Download PDF