Dual-Cascaded Multi-Task Learning for Mobility Management in mmWave Wireless Systems

For millimeter-wave wireless systems, mobility management is crucial to facilitate high-quality services in dynamic scenarios, which generally includes the tasks of base station prediction, beam tracking, and positioning. Existing schemes usually adopt deep learning to separately implement these tasks to support mobility management, which suffer from the performance loss when training samples are inadequate. To address this issue, a dual-cascaded multi-task structure is proposed in this letter, which could simultaneously utilize the labeled data from various tasks to realize joint training based on their inter-task correlations. Specifically, the deep learning models for different tasks are connected through the dual-layer cascaded and residual design, enabling efficient information interaction among different tasks. Besides, a dedicated training scheme based on loss descending rate is proposed for effective multi-task optimization. Simulation results demonstrate that our proposed multi-task learning scheme significantly outperforms its conventional counterparts in mobility management, especially for dynamic wireless scenarios with inadequate training samples.