Weather Estimation for Integrated Sensing and Communication
Victoria Palhares, Artjom Grudnitsky, Silvio Mandelli

TL;DR
This paper demonstrates that 6G base stations can be repurposed for accurate weather sensing using deep learning, enabling cost-effective and widespread weather monitoring as a new service in future networks.
Contribution
It introduces a novel approach to utilize 6G infrastructure for weather sensing through neural networks, expanding ISAC capabilities beyond object detection.
Findings
Achieved over 99% accuracy in classifying weather conditions.
Estimated precipitation rate and wind speed with errors of 1.2 mm/h and 1.5 km/h.
Validated the approach with a multi-week experimental campaign.
Abstract
One of the key features of sixth-generation (6G) mobile communications will be integrated sensing and communication (ISAC). While the main goal of ISAC in standardization efforts is to detect objects, the byproducts of radar operations can be used to enable new services in 6G, such as weather sensing. Even though weather radars are the most prominent technology for weather detection and monitoring, they are expensive and usually neglect areas in close vicinity. To this end, we propose reusing the dense deployment of 6G base stations for weather sensing purposes by detecting and estimating weather conditions. We implement both a classifier and a regressor as a convolutional neural network trained across measurements with varying precipitation rates and wind speeds. We implement our approach in an ISAC proof-of-concept and conduct a multi-week experiment campaign. Experimental results…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrecipitation Measurement and Analysis · Radar Systems and Signal Processing · Radio Wave Propagation Studies
