NSF Award
2316865
Radio frequency (RF) sensing is becoming increasingly pervasive due to the proliferation of wireless communications. Remarkably, the upcoming WiFi standard IEEE802.11bf will enable wireless devices to function as radars to sense the surroundings and determine the position of nearby objects. Most RF sensors, however, are unable to look around the corner and locate non-line-of-sight targets. To address this problem, a commonly employed approach involves deploying multiple RF transmitters and receivers across the surveillance area, so that the target can be observed by at least one transmitter-receiver pair. Unfortunately, such a distributed RF sensing system is bulky, costly, operationally burdensome, and environmentally unfriendly due to excessive radiation. To overcome these shortcomings, this project aims to develop an alternative distributed RF sensing paradigm that can effectively “look” around the corner by leveraging a reconfigurable intelligent surface (RIS), which is a thin planar structure comprising numerous small low-cost metamaterial elements that can be independently adjusted to control the reflection of incident RF signals. Like wallpaper, RIS can cover parts of buildings, walls, and ceilings, allowing RF engineers to proactively customize the radio environment based on specific needs. This work will advance fundamental theory and practical methods for RF sensing aided by RIS. Research outcomes of this project will have the potential to be integrated with future wireless networks, empowering service providers to offer intelligent RF sensing and communication services to their customers. <br/><br/>The objective of this project is to establish a systematic signal processing framework for ubiquitous RF sensing aided by RIS in distributed environments. In these settings, the transmitter and receiver may be co-located or spatially distributed, with RISs deployed to assist in target illumination and/or observation. The proposed framework encompasses both active sensing, where a dedicated transmitter is jointly optimized as a part of the system, and passive sensing, which employs ambient wireless sources, such as cellular and WiFi signals, to probe the environment. The research efforts are structured into three thrusts. Thrust 1 focuses on system design and optimization, taking into account multipath and asynchronous propagation inherent in the distributed sensing system. Thrust 2 develops training-efficient channel estimation techniques, including methods to estimate the cascade channel that serially links the transmitter, RIS, targets, and receiver, as well as approaches for estimating the statistics of the cascade channel. Lastly, Thrust 3 explores RF sensing using RIS with a multi-layer structure, which provides additional RF signal processing capabilities, while simultaneously reducing hardware complexity and energy consumption.<br/><br/>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.
