Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc.). Inspired by the great success of using a neural network to describe the optical field in computer vision, we propose a neural radio-frequency radiance field, NeRF², which represents a continuous volumetric scene function that makes sense of an RF signal's propagation. Particularly, after training with a few signal measurements, NeRF² can tell how/what signal is received at any position when it knows the position of a transmitter. As a physical-layer neural network, NeRF² can take advantage of the learned statistic model plus the physical model of ray tracing to generate a synthetic dataset that meets the training demands of application-layer artificial neural networks (ANNs). Thus, we can boost the performance of ANNs by the proposed turbo-learning, which mixes the true and synthetic datasets to intensify the training. Our experiment results show that turbo-learning can enhance performance with an approximate 50% increase. We also demonstrate the power of NeRF² in the field of indoor localization and 5G MIMO.

Conventional vibration sensing systems, equipped with specific sensors (e.g., accelerometer) and communication modules, are either expensive or cumbersome in deployment. In recent years, the community revisits this classic topic by taking advantage of off-the-shelf RFIDs. However, limited by lower reading rate and larger wavelength, current RFID based solutions can only sense low-frequency (e.g. below 100Hz) mechanical vibrations with larger amplitude (e.g. (>) 5mm). To address this issue, this work presents TagSound, an RFID-based vibration sensing system that explores a tag's harmonic backscattering to recover high-frequency and tiny mechanical vibrations accurately. The key innovations are in two aspects: harmonics based sensing and a new recovery scheme. We implement TagSound with USRP platforms. Our comprehensive evaluation shows TagSound can achieve a mean error of 0.37 Hz when detecting vibrations at frequencies below 100Hz, and a mean error of 4.2 Hz even when the vibration frequency is up to 2500Hz.

Conventional vibration sensing systems, equipped with specific sensors (e.g., accelerometer) and communication modules, are either expensive or cumbersome to deploy. Recently research community revisits this classic topic by taking advantage of off-the-shelf RFIDs. However, limited by low reading rate and long wavelength, current RFID based solutions can only sense low-frequency (e.g., below 100 Hz) mechanical vibrations with larger amplitude (e.g., $>\!\!5$ mm). To address the issue, this work presents TagSound, an RFID-based vibration sensing system that explores a tag's harmonic backscattering to recover high-frequency and tiny mechanical vibrations accurately. The key innovations are in two aspects: harmonics based sensing and a new recovery scheme . We implement TagSound with USRP platforms. Our comprehensive evaluation shows (i) TagSound can achieve a mean error of 0.37 Hz when detecting vibrations at frequencies below 100 Hz, and a mean error of 4.2 Hz even when the vibration frequency is up to 2500 Hz. (ii) TagSound can achieve a Hz-level frequency estimation even when the vibration amplitude is only 2 mm.

Deployment of billions of commercial off-the-shelf (COTS) radio frequency identification (RFID) tags has drawn much of the attention of the research community because of the performance gaps of current systems. In particular, hash-enabled protocol (HEP) is one of the most thoroughly studied topics in the past decade. HEPs are designed for a wide spectrum of notable applications (e.g., missing detection) without need to collect all tags. HEPs assume that each tag contains a hash function, such that a tag can select a random but predictable time slot to reply with a one-bit presence signal that shows its existence. However, the hash function has never been implemented in COTS tags in reality, which makes HEPs a ten-year untouchable mirage. This paper designs and implements a group of analog on-tag hash primitives (called Tash) for COTS Gen2-compatible RFID systems, which moves prior HEPs forward from theory to practice. In particular, we design three types of hash primitives, namely, tash function, tash table function, and tash operator. All of these hash primitives are implemented through the selective reading, which is a fundamental and mandatory functionality specified in Gen2 protocol, without any hardware modification and fabrication-a feature allowing zero-cost fast deployment on billions of Gen2 tags. We further apply our hash primitives in one typical HEP application (i.e., missing detection) to show the feasibility and effectiveness of Tash. Results from our prototype, which is composed of one ImpinJ reader and 3000 Alien tags, demonstrate that the new design lowers 70% of the communication overhead in the air. The tash operator can additionally introduce an overhead drop of 29.7%.

An ultrasonic Positioning System (UPS) has outperformed RF-based systems in terms of its accuracy for years. However, few of the developed solutions have been deployed in practice to satisfy the localization demand of today's smart devices, which lack ultrasonic sensors and were considered as being "deaf" to ultrasound. A recent finding demonstrates that ultrasound may be audible to the smart devices under certain conditions due to their microphone's nonlinearity. Inspired by this insight, this work revisits the ultrasonic positioning technique and builds a practical UPS, called UPS+, for ultrasound-incapable smart devices. The core concept is to deploy two types of indoor beacon devices, which will advertise ultrasonic beacons at two different ultrasonic frequencies respectively. Their superimposed beacons are shifted to a low-frequency by virtue of the nonlinearity effect at the receiver's microphone. This underlying property functions as an implicit ultrasonic downconverter without throwing harm to the hearing system of humans. We demonstrate UPS+, a fully functional UPS prototype, with centimeter-level localization accuracy using custom-made beacon hardware and well-designed algorithms.