Cross-Medium Transmission With Mobile AUV Relay Path Planning: Enhancing Underwater Acoustic and Radio Mixed-Link Performance

Marine ecological monitoring, underwater unmanned device surveillance, and resource exploration are increasingly dependent on underwater autonomous systems. Consequently, efficient information transmission to onshore command centers is imperative. Converting underwater acoustic (UWA) signals to radio signals through surface relaying is considered the most efficient method for long-range cross-medium (water and air) transmission. Traditionally, static floating nodes serve as relays in such systems. In this study, we propose a mobile cross-medium relay scheme utilizing autonomous underwater vehicles (AUVs). This approach capitalizes on two key factors: the physical differences between the two mediums (water and air) and the mobility of the AUV. By leveraging these, it improves the UWA links, thereby improving cross-medium transmission performance. Our scheme integrates a spatially varying UWA channel model, commonly employed in physical-layer studies, into network performance analysis, considering the inhomogeneous underwater medium. Within this framework, we optimize the cross-medium transmission performance of dual-hop links by determining the optimal mobile relay path. The AUV can operate on the surface, acting as an acoustic-to-radio signal relay, or navigate underwater, adapting its path to the spatial variations in UWA channels. This adaptability enables more efficient underwater data collection. To identify the most effective mobile relaying paths, we propose a cross-medium relay path planning based on swarm intelligence and reinforcement learning (RL) algorithms. Simulations demonstrate that our proposed mobile relay transmission scheme outperforms static relay systems, achieving higher cross-medium transmission data length, improved end-to-end data rates, and better balance between the two links. Furthermore, RL-based path planning yields superior performance compared to ant colony optimization (ACO)-based planning.