Intelligent Dynamic Handover via AI-assisted Signal Quality Prediction in 6G Multi-RAT Networks

TL;DR

This work tackles the challenge of unreliable and reactive handovers in fast-changing 6G multi-RAT HetNets by introducing Predictive Conditional Handover (P-CHO), which leverages short-horizon, model-driven SINR forecasts to drive proactive handovers. RAT-aware LSTM predictors (BiLSTM for cellular BSs and a lightweight LSTM for WiFi APs) are orchestrated by a RAT Steering Controller to generate per-RAT predictions that feed a prediction-conditioned decision policy with hysteresis to mitigate ping-pong. Direct multi-step SINR forecasting outperforms recursive approaches, and hysteresis-enabled P-CHO reduces handover signaling while preserving connectivity, as demonstrated in a Python-based mixed Cellular/WiFi simulator. The framework enables low-latency, proactive mobility management and can be extended to O-RAN, federated learning, and multi-objective optimization for scalable, edge-empowered 6G networks.

Abstract

The emerging paradigm of 6G multiple Radio Access Technology (multi-RAT) networks, where cellular and Wireless Fidelity (WiFi) transmitters coexist, requires mobility decisions that remain reliable under fast channel dynamics, interference, and heterogeneous coverage. Handover in multi-RAT deployments is still highly reactive and event-triggered, relying on instantaneous measurements and threshold events. This work proposes a Machine Learning (ML)-assisted Predictive Conditional Handover (P-CHO) framework based on a model-driven and short-horizon signal quality forecasts. We present a generalized P-CHO sequence workflow orchestrated by a RAT Steering Controller, which standardizes data collection, parallel per-RAT predictions, decision logic with hysteresis-based conditions, and CHO execution. Considering a realistic multi-RAT environment, we train RAT-aware Long Short Term Memory (LSTM) networks to forecast the signal quality indicators of mobile users along randomized trajectories. The proposed P-CHO models are trained and evaluated under different channel models for cellular and IEEE 802.11 WiFi integrated coverage. We study the impact of hyperparameter tuning of LSTM models under different system settings, and compare direct multi-step versus recursive P-CHO variants. Comparisons against baseline predictors are also carried out. Finally, the proposed P-CHO is tested under soft and hard handover settings, showing that hysteresis-enabled P-CHO scheme is able to reduce handover failures and ping-pong events. Overall, the proposed P-CHO framework can enable accurate, low-latency, and proactive handovers suitable for ML-assisted handover steering in 6G multi-RAT deployments.

Figures (17)

• Figure 1: Multi-RAT HetNet with co-existing cellular/WiFi coverage. Dashed blue curve reflects a UE random-walk path.
• Figure 2: LSTM structure for timeseries signal quality prediction.
• Figure 3: Proposed three-stage P-CHO sequence callflows.
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