The real-time knowledge of mmWave device orientation offers dual benefits for wireless networks and Internet of Things (IoT) applications: it enhances communication and localization performance through link diagnosis, and it enables context inference with asset and wearable tracking. This paper presents MetaPol, a novel system architecture that augments commodity mmWave access points (APs) with a carefully designed ultra-low-cost anisotropic metasurface to non-invasively extract the orientation of client devices, without hindering data reception or modifying hardware. MetaPol leverages the polarization of transmitted electromagnetic (EM) waves as an accurate indicator of the orientation of linearly polarized antenna arrays, prevalent in commercial mmWave mobile and IoT devices. Yet, polarization sensing is rarely supported by commodity APs due to the need for two orthogonally polarized antenna arrays to capture the incident electric field. Instead, MetaPol creates virtual polarization channels to characterize impinging electric fields, through the conversions of wave polarization on the fly as it interacts with the metasurface. To design MetaPol, we model and exploit the properties of a unique anisotropic metamaterial based on C-shaped split-ring resonators. We discover that, when grouped in certain configurations, these meta-elements can convert the incident polarization in a deterministic way. We show that three polarization channels are sufficient for unambiguous orientation sensing, and we provide a corresponding three-shot non-coherent protocol that extracts user orientation by comparing the power received from distinct surface areas. Through extensive over-the-air experiments with more than 1000 measurements in the mmWave band, we demonstrate that MetaPol achieves a mean error of 2.6° across practical settings with negligible compromise to the underlying data communication link.

The integration of communication and sensing functions within mmWave systems has gained attention due to the potential for enhanced passive sensing and improved communication reliability. State-of-the-art techniques separate these two functions in frequency, use of hardware, or time, i.e., sending known preambles for channel sensing or unknown symbols for communications. In this paper, we introduce Panoptic, a novel system architecture for integrated communication and sensing sharing the same hardware, frequency, and time resources. Panoptic jointly detects unknown symbols and channel components from data-modulated signals. The core idea is a new beam manipulation technique, which we call compressive sidelobe forming, that maintains a directional mainlobe toward the intended communication nodes while acquiring unique spatial information through pseudorandom sidelobe perturbations. We implemented Panoptic on 60 GHz mmWave radios and conducted extensive over-the-air experiments. Our results show that Panoptic achieves reflector angular localization error of less than 2° while at the same time supporting mmWave data communication with a negligible BER penalty when compared with conventional communication-only mmWave systems.

Sensing friction coefficient is vital for various cyber-physical system applications, including robotic grasping. We present RoboTera, a novel system for the non-contact coefficient of friction (COF) estimation using sub-Terahertz (sub-THz) perception in robotics for the first time. While advanced tactile sensors can provide friction inputs, they require direct contact, which might not be suitable for various applications. Non-contact estimation of friction between the gripper and a target object requires extracting the minute surface perturbations which is unfortunately not supported by existing imaging modalities (such as camera and LiDAR). Our key insight is that sub-THz signals are best suited to infer such information as their sub-millimeter wavelength is comparable with surface perturbations. Hence, impinging sub-THz waves on everyday objects creates diffuse backscattering whose spectral profile hints at surface texture properties. Leveraging this, we use sub-THz wireless signals to extract surface roughness. By integrating sub-THz-estimated roughness inputs with conventional image-based material classification schemes, RoboTera provides a non-contact and precise COF inference framework. Further, we exploit COF inferences to identify stable grasp configurations and improve grasping performance. Our experiments demonstrate an average accuracy of over 92% in COF estimation. We implemented RoboTera on a robotic arm to assess its real-world grasping performance, achieving a 31.8% average improvement across objects with diverse COF profiles and shapes.

Sensing the coefficient of friction (COF) is crucial for robotic and Cyber-Physical System applications, including grasping. We introduce RoboTera, a novel system for non-contact COF estimation using sub-Terahertz (sub-THz) perception in robotics. Unlike tactile sensors that require direct contact, our approach leverages sub-THz signals with sub-millimeter wavelength to capture surface roughness characteristics as an essential factor in non-contact COF inference, that conventional imaging modalities like cameras and LiDAR cannot detect. Our system enables precise COF inference by integrating sub-THz-estimated roughness with image-based material classification. Further, we exploit COF inferences to identify stable grasp configurations and improve grasping performance. Experiments show over 92% accuracy in COF estimation, with a 31.8% improvement in grasp success rates in real-world robotic tasks.

Owing to the substantial bandwidth they offer, the exploration of 100+ GHz frequencies for wireless communications has surged in recent years. These sub-Terahertz channels are susceptible to blockage, which makes reflected paths crucial for seamless connectivity. However, at such high frequencies, reflections deviate from the known mirror-like specular behavior as the signal wavelength becomes comparable to the height perturbation at the surface of the reflectors. Such reflectors are considered electromagnetically “rough” which results in random non-specular reflection components that are not well understood. In this paper, we delve into the fundamentals of rough scattering to analyze its implications for sub-THz wireless networks, including the existence and strength of non-specular links, mobility resilience, and beam reciprocity. Further, we present a novel framework that re-purposes IEEE 802.11ay-like beam sweeps for estimating the surface roughness of a reflector in the vicinity of the communication nodes. Through extensive modeling, simulation, and experiments with everyday reflector samples, we demonstrate the impact of rough scattering on over-the-air data links and evaluate the accuracy of our roughness inference framework.

mmWave antenna array calibration is a necessary yet tedious and costly process in manufacturing to capture the non-idealities in phased arrays, in order to obtain codebooks for accurate and stable beam steering. Unfortunately, predefined codebooks provided by manufacturers to steer beams in a given set of directions do not support the arbitrary beam shapes required for various mmWave communication, sensing, and security applications. To create arbitrary beam patterns, one needs to first find the unknown calibration vector for the particular phased array in use. In this paper, we introduce EiCal, a novel zero-shot technique that leverages the beamforming codebook advertised by the manufacturer to extract the calibration vector at zero cost (i.e., with no additional measurements). The key idea is that the unknown desired calibration vector can be obtained via an appropriately designed eigen-decomposition of the given codebook. We experimentally demonstrate the efficacy of EiCal on a 60 GHz mmWave array for two scenarios: angle estimation using compressive pseudorandom beams, and simultaneous steering of beams and nulls. Our results also point to potential simplifications in the calibration process at the manufacturer.

The use of frequencies above 100 GHz has received increasing attention due to the large amount of available bandwidth. Given the high chance of signal blockage, reflected paths play a key role in 100+ GHz networks. Interestingly, at these frequencies, the signal wavelength becomes comparable to the height perturbation in common natural surfaces. Hence, the reflection pattern deviates from mirror-like smooth reflection and exhibits rough scattering patterns that consist of non-specular components. This paper presents an in-depth analysis and experimental demonstration of "rough" surface scattering and its implications for wireless networking, namely in coverage, mobility resilience, and channel reciprocity. Furthermore, we present a novel framework for estimating the surface roughness level from the unique spectral and spatial signatures seen in the reflection spectra. We perform extensive modeling, simulation, and over-the-air experiments using a broadband sub-THz wireless system employing typical indoor/outdoor surfaces such as tile, brick, redstone, and granite.