mmWall

Cho, Kun Woo; Mazaheri, Mohammad Hossein; Gummeson, Jeremy; Abari, Omid; Jamieson, Kyle

doi:10.1145/3446382.3448665

Cited by 25 publications

(5 citation statements)

References 16 publications

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“…In future work, we will extend coVRage to the scenario where multiple APs cover the HMDs [61]. In addition, we envision an enhancement using Intelligent Reflective Surfaces [62], which could provide LoS communication even when such a link is not directly available between AP and HMD, expected to occur mainly when a user looks up or down.…”

Section: Discussionmentioning

confidence: 99%

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

Struye

Lemić

Famaey

2023

IEEE Trans. Wireless Commun.

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Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). "Cutting the wire" in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeterwave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna's orientation. As such, beamforming must adapt, in real-time, to the user's head rotations. An HMD's built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using subarrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.

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Section: Discussionmentioning

confidence: 99%

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

Struye

Lemić

Famaey

2023

IEEE Trans. Wireless Commun.

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“…For example, MilliMirror [188] designs a passive metasurface prototype which expands the coverage of mmWave radios to blind spots by redirecting and reshaping mmWave signals to any anomalous directions. mmWall [189] proposes another tunable smart surface made of metamaterial, which enables a fast mmWave beam relay through the wall and redirects the beam power to another direction when a human body blocks a lineof-sight path. In addition, mmWall also supports splitting the incoming signal into multiple beams and concurrently steering the multi-armed beams.…”

Section: Integration With New Mediumsmentioning

confidence: 99%

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

Zhang,

Xi,

et al. 2023

IEEE Commun. Surv. Tutorials

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With the rapid development of the Internet of Things (IoT) and the rise of 5G communication networks and automatic driving, millimeter wave (mmWave) sensing is emerging and starts impacting our life and workspace. mmWave sensing can sense humans and objects in a contactless way, providing fine-grained sensing ability. In the past few years, many mmWave sensing techniques have been proposed and applied in various human sensing applications (e.g., human localization, gesture recognition, and vital monitoring). We discover the need of a comprehensive survey to summarize the technology, platforms and applications of mmWave-based human sensing. In this survey, we first present the mmWave hardware platforms and some key techniques of mmWave sensing. We then provide a comprehensive review of existing mmWave-based human sensing works. Specifically, we divide existing works into four categories according to the sensing granularity: human tracking and localization, motion recognition, biometric measurement and human imaging. Finally, we discuss the potential research challenges and present future directions in this area.

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“…Besides, mmWave sensing is also applied to interact with metamaterial tags enabling new paradigms and applications, such as tagging infrastructure [49], wireless temperature monitoring [27], and through-wall communication [30], [29]. To our best knowledge, MetaWave is the first work to utilize the metamaterial-enhanced tags to attack mmWave sensing passively.…”

Section: Related Workmentioning

confidence: 99%

“…Note: the dashed line in sensor reading (right side) is natural, and the solid line represents tampered. material tags can passively modulate RF signals for communication and networking applications [30], [31], [29]. Metamaterial is a new class of functional materials designed around unique patterns or structures, which cause them to interact with RF signals and other forms of energy in ways not found in nature [92], [90].…”

Section: Introductionmentioning

confidence: 99%

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

Chen¹,

Li²,

Chen³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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Millimeter-wave (mmWave) sensing has been applied in many critical applications, serving millions of thousands of people around the world. However, it is vulnerable to attacks in the real world. These attacks are based on expensive and professional radio frequency (RF) modulator-based instruments and can be prevented by conventional practice (e.g., RF fingerprint). In this paper, we propose and design a novel passive mmWave attack, called MetaWave, with low-cost and easily obtainable meta-material tags for both vanish and ghost attack types. These meta-material tags are made of commercial offthe-shelf (COTS) materials with customized tag designs to attack various goals, which considerably low the attack bar on mmWave sensing. Specifically, we demonstrate that tags made of ordinal material (e.g., C-RAM LF) can be leveraged to precisely tamper the mmWave echo signal and spoof the range, angle, and speed sensing measurements. Besides, to optimize the attack, a general simulator-based MetaWave attack framework is proposed and designed to simulate the tag modulation effects on the mmWave signal with advanced tag and scene parameters. We evaluate, MetaWave, the meta-material tag attack in both simulation and real-world experiments (i.e., 20 different environments) with various attack settings. Experimental results demonstrate that MetaWave can achieve up to 97% Top-1 attack accuracy on range estimation, 96% on angle estimation, and 91% on speed estimation in actual practice, 10-100X cheaper than existing mmWave attack methods. We also evaluate the usability and robustness of MetaWave under different real-world scenarios. Moreover, we conduct in-depth analysis and discussion on countermeasures for MetaWave mmWave attacks to improve wireless sensing and cyber-infrastructure security.

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mmWall

Cited by 25 publications

References 16 publications

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

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