Collaborative Research: CCSS: Rethinking Wireless Channel Through the Lens of Radiance Field

About This Grant

Accurate and timely channel state information (CSI) is essential for the performance of next-generation wireless systems, particularly those operating in high-frequency bands with large-scale antenna arrays. These systems, including future 6G networks, rely on spatially resolved CSI to support tasks such as beamforming, mobility management, and interference mitigation. However, acquiring reliable CSI in practical environments remains challenging due to high measurement cost, environmental complexity, and real-time constraints. This project develops a modeling framework that integrates limited radio measurements with spatial priors derived from environmental sensing. Specifically, the project investigates how geometric and visual information can be used to infer signal behavior in environments with constrained sensing capability. Rather than introducing a new channel abstraction, the project focuses on applying radiance-inspired modeling to characterize local electromagnetic behavior as a function of position and direction. The resulting models aim to support compact, data-efficient CSI reconstruction for structured scenarios such as indoor or urban deployments. Broader impacts include the integration of project outcomes into advanced wireless curriculum and engagement of students through interdisciplinary research. Project data, code, and validation tools will be open-sourced to support reproducibility and research dissemination. The proposed research explores a data fusion framework for reconstructing spatially varying signal behavior using sparse CSI measurements and environmental priors. The approach involves applying array signal processing techniques to derive location-specific channel measurements and aligning them with 3D environmental layouts obtained through vision-based reconstruction. The resulting signal model is localized and designed to approximate the directional energy distribution of wireless signals in space. The framework supports efficient channel prediction under constrained deployment settings and is applicable to emerging mmWave and sub-THz systems. The project also includes experimental validations across different frequency bands. Emphasis is placed on practical challenges such as limited sensor coverage, partial line-of-sight, and real-time inference under sparse signal measurements. The research outcomes are expected to inform the development of practical CSI estimation tools for deployment in complex high-frequency wireless environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.