Sim-to-Real Transfer for Estimation over Wireless Networks

Data-driven models for state estimation using measurements obtained over a wireless network are essential to cyber-physical systems. Learning a data-driven model for an estimator using real wireless network deployments is, however, impractical as it would require data that captures varied wireless network conditions and their impact on estimation. We propose and evaluate a simulation to real-world transfer of a data-driven model for state estimation. Specifically, we train an estimator model using only data generated by a low-fidelity simulation of networks. Our choice of network simulation is a first-come-first-served single server queue, which is a network model with the two parameters of arrival rate of measurement packets into the queue and packet service rates. We employ domain randomization to bridge the gap between simulation and the real world, appropriately randomizing the network model parameters during training. The efficacy of the resulting estimator model is demonstrated by testing it over two deployments of real wireless networks. In one, the estimator model estimates vehicles’ positions and speeds using data from vehicular trajectories received by it over a shared WiFi network, with up to seventy sources sending the measurements. In the other, GPS coordinates are communicated by public transit buses over city-wide cellular networks. The estimator uses the received measurements to estimate locations of the buses.