RIS-Based Radio Localization in Rich Scattering Environments: Harnessing Multi-Path with ANN Decoders

Abstract

Radio localization is a key enabling technology for situational awareness but conventional techniques based on elaborate ray-tracing approaches naturally struggle in rich scattering environments (inside rooms, metro stations, planes, vessels, …). Here, we discuss a completely different approach to radio localization: instead of attempting to understand rich scattering wave propagation in terms of rays, we harness the overwhelming complexity because it assigns unique wave finger-prints to each object position. We interpret wave propagation as a physical encoder of the sought-after localization information in multiplexed measurements and detail artificial neural network (ANN) architectures suitable to decode these measurements for a single or multiple, discrete or continuous, sought-after location variable(s). Capitalizing on recent physics-driven experiments, we clarify that the proposed technique is very robust to measurement noise and capable of achieving deeply sub-wavelength localization precision. The discussed technique can be implemented with multiplexing across spatial, spectral or configurational degrees of freedom, corresponding to sensor networks, broadband measurements and RIS-programmable environments, respectively. Specifically, multiplexing across a fixed random sequence of RIS configurations enables single-frequency localization with a single node. Finally, we propose an end-to-end vision of the technique in which programmable RIS elements take the role of physical weights in a hybrid analog-digital ANN. Thereby, relevant information for the localization task can be discriminated from irrelevant information already in the measurement process, enabling substantial latency improvements.