Context-Aware Hierarchical Learning for Mobile Relay Control in mmWave 6G-IoT Networks

While millimeter-wave (mmWave) communication in emerging Sixth Generation (6G) networks offers high bandwidth for the Internet of Things (IoT), it is highly susceptible to blockages, necessitating intelligent relay positioning. Current static relay selection methods are typically unable to adapt to dynamic blockage conditions in IoT deployments, leading to frequent connectivity outages. In this paper, we address this by introducing Hierarchical Mobile Adaptive Relay Control (H-MARC), a reinforcement-learning framework for intelligent mobile relay positioning in IoT networks. H-MARC decomposes relay positioning into strategic long-term planning and tactical real-time control using Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithms. We further present a context-aware extension of H-MARC, referred to as H-MARC-C, by exploiting WiFi context information for predictive blockage detection through cross-band correlation analysis. Computer-based simulations demonstrate that H-MARC achieves 4.2 bits/s/Hz spectral efficiency with 87% connection reliability, while H-MARC-C attains 4.8 bits/s/Hz with 93% reliability representing 35% and 55% improvements over static methods. The framework reduces blockage adaptation time from 2.3s to 0.3s and achieves 80% higher energy efficiency (4.2×106bits/J) compared to reactive approaches, with 24% faster convergence than flat RL (reinforcement learning) baselines, enabling ultra-reliable communications for demanding IoT applications including industrial automation and smart cities.