Learning from Radio using Variational Quantum RF Sensing
This work addresses the challenge of environmental sensing in wireless networks for applications like localization, though it appears incremental as it builds on quantum sensing methods applied to radio signals.
The paper tackled the problem of learning about the environment from radio signals using quantum sensing, showing that this approach enables intelligent systems to operate without channel measurements at deployment and remain sensitive to weak signals, achieving results in a localization task under realistic conditions.
In modern wireless networks, radio channels serve a dual role. Whilst their primary function is to carry bits of information from a transmitter to a receiver, the intrinsic sensitivity of transmitted signals to the physical structure of the environment makes the channel a powerful source of knowledge about the world. In this paper, we consider an agent that learns about its environment using a quantum sensing probe, optimised using a quantum circuit, which interacts with the radio-frequency (RF) electromagnetic field. We use data obtained from a ray-tracer to train the quantum circuit and learning model and we provide extensive experiments under realistic conditions on a localisation task. We show that using quantum sensors to learn from radio signals can enable intelligent systems that require no channel measurements at deployment, remain sensitive to weak and obstructed RF signals, and can learn about the world despite operating with strictly less information than classical baselines.