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.

翻译：在6G多无线接入技术（多RAT）网络这一新兴范式中，蜂窝与无线保真（WiFi）发射器共存，需要移动性决策在快速信道动态、干扰和异构覆盖下保持可靠。当前多RAT部署中的切换仍高度依赖即时测量和阈值事件，具有反应式和事件触发特性。本研究提出一种基于机器学习（ML）辅助的预测性条件切换（P-CHO）框架，该框架利用模型驱动和短时域信号质量预测。我们提出一种由RAT引导控制器编排的通用P-CHO序列工作流程，标准化了数据收集、并行按RAT预测、基于滞后条件的决策逻辑以及CHO执行流程。考虑现实多RAT环境，我们训练具备RAT感知能力的长期短期记忆（LSTM）网络，以预测沿随机轨迹移动用户的信号质量指标。所提出的P-CHO模型在蜂窝与IEEE 802.11 WiFi融合覆盖的不同信道模型下进行训练与评估。我们研究了不同系统设置下LSTM模型超参数调优的影响，并比较了直接多步与递归P-CHO变体。同时与基线预测器进行了对比测试。最后，在软切换与硬切换设置下验证所提P-CHO方案，结果表明启用滞后机制的P-CHO方案能有效降低切换失败与乒乓事件。总体而言，所提出的P-CHO框架能够实现精准、低延迟、主动式的切换机制，适用于6G多RAT部署中ML辅助的切换引导。