A Simulation Platform for Learning Empowered Distributed Sensing

Emerging communication technologies, such as millimeter-wave (mmWave) and massive antenna arrays, facilitate highly directional and long-range communication through beamforming and Multiple-Input Multiple-Output (MIMO) techniques. Recent AI advancements hold significant promise for enhancing Radio Frequency (RF) tracking capabilities, enabling the detection, localization, and tracking of highly directional signals through coordinated swarms. However, these advancements also bring new challenges, such as the need for comprehensive training datasets that account for various environmental factors affecting RF signal propagation, including diverse weather conditions, buildings, and terrains. This paper introduces a new simulation platform specifically for evaluating the performance of RF tracking methods and, more importantly, generating comprehensive signal map training datasets for reinforcement learning-based RF tracking algorithms. Leveraging the MATLAB RF signal simulation toolbox, the simulator possesses the capability to model RF signal propagation and swarm mobility, accounting for diverse factors such as free space loss (due to propagation distance), diffraction loss (due to obstacle obstruction), and environmental variables like terrain, buildings, and weather conditions (e.g., sunny, cloudy, and foggy). Additionally, the platform can simulate the trajectories of different types of moving transmitters and receivers (such as robots, drowns, and vehicles). Furthermore, the simulator offers users and developers flexibility to incorporate their own mobility models into the simulation environment, including control mobility models and data-driven models (e.g., transformer), enabling the training of reinforcement learning for RF tracking in complex scenarios generated by the platform.