Neural Radiance Fields (NeRF) have emerged as a powerful technique for synthesizing novel views of complex 3D scenes from a sparse set of images. The advances in NeRF has shown prominence in the field of wireless networks as well. This paper explores the application of NeRF in the domain of spectrum sensing, proposing a novel approach that leverages the capabilities of RF based NeRF and extend it to enhance the accuracy and efficiency of spectrum sensing in wireless communication networks. Our proposed solution SpecNeRF is evaluated through extensive experiments, demonstrating significant improvements in terms of scalability, robustness to environmental changes, and adaptability to varying signal conditions. SpecNeRF not only provides a viable solution for current spectrum sensing challenges but also paves the way for innovative applications in future wireless networks, including cognitive radio and 6G technologies.

The demand for real-time and accurate mapping is ubiquitous, particularly in complex indoor settings. While SLAM-based methods are popular, Radio Tomographic Imaging (RTI) offers an essential set of advantages, including mapping inaccessible or enclosed spaces, shorter scanning trajectories, or even identifying material properties of structures on the map. However, existing RTI systems typically depend on pre-deployed, precisely calibrated infrastructure with ample computing power, making it challenging to deploy in a ubiquitous setting. We designUbiqMap, a lightweight RTI-based end-to-end system capable of mapping indoor spaces in real-time, with minimal to zero reliance over pre-deployed infrastructure. We evaluate the performance ofUbiqMapin various scenarios, including two real deployments - a moderately complex residential apartment (800 sq. ft) and a large building foyer area (3000 sq. ft) and a few simulated scenarios. We demonstrate howUbiqMapcan benefit over traditional SLAM-based techniques in specific contexts and advocate the fusion of RTI methods with SLAM to improve future mapping technologies. Overall,UbiqMapimproves the quality of the estimated map by 30%–40% over the state-of-the-art with equivalent resource availability.