Scalable 3D Beam-Steering for Directional Millimeter Wave Wireless Networks

Yang, Yi; Zhou, Anfu; Wu, Leilei; Xü, Dianguo; Ma, Huadóng; Wei, Teng; Zhang, Xinyu

doi:10.1109/twc.2021.3098729

Cited by 2 publications

(6 citation statements)

References 25 publications

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“…General beamforming solutions commonly rely on some sort of search algorithm to determine a high-gain beam, possibly from a predefined set of beams [6]- [8]. As the steering vector is easily derived from pose information, a beam focused in the LoS direction can be synthesized directly and efficiently by simply setting the AWV in (2) to the steering vector in (7). This maximizes the gain in (13), as b H a ≤ 1 and a H a = 1 for any normalized a and b.…”

Section: A Challenges and Opportunitiesmentioning

confidence: 99%

“…2) Sub-Beamforming: Once the AP trajectory as seen from the HMD is determined, the algorithm needs to synthesize a beam covering it. For this step, we represent directions not as the commonly used azimuth-elevation pair (θ, ψ), but rather in UV-coordinates (u, v) with u = cos ψ sin θ and v = sin ψ [7]. While beamwidth depends on the angular distance from broadside for azimuth-elevation (see (14)), the beamwidth is nearly invariant to the beam's direction with UVcoordinates [46].…”

Section: A Sub-beam Generationmentioning

confidence: 99%

“…The optimal AWV w pointing to (θ, ψ), ignoring NLoS paths, is easily derived from the channel model. The gain in some AoA (θ, ψ) is maximized by setting the AWV (before normalization) equal to the steering vector for that AoA, defined in (7). For the full AWV of coVRage's entire beam, which aims at several directions, it suffices to, for each element (x, y), set its AWV element w x,y to the steering vector for the AoA of the sub-beam formed by the sub-array that element belongs to.…”

Section: A Sub-beam Generationmentioning

confidence: 99%

“…For HMDs specifically, accurate on-device pose estimation [11] enables direct beamforming between the mobile HMD and static Access Point (AP). Several works have leveraged this pose information for beamforming [7], [12]- [14], noting that HMD-side beamforming is considerably more challenging than at the AP side. An angular beam misalignment of a few degrees can have a significant impact on Signal-to-Noise Ratio (SNR) [12], which is most rapidly triggered by head rotations [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several works have leveraged this pose information for beamforming [7], [12]- [14], noting that HMD-side beamforming is considerably more challenging than at the AP side. An angular beam misalignment of a few degrees can have a significant impact on Signal-to-Noise Ratio (SNR) [12], which is most rapidly triggered by head rotations [7]. All of the solutions only generate a beam in a single direction, based on a current pose estimate.…”

Section: Introductionmentioning

confidence: 99%

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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: A Challenges and Opportunitiesmentioning

confidence: 99%

Section: A Sub-beam Generationmentioning

confidence: 99%

Section: A Sub-beam Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

Struye

Lemić

Famaey

2023

IEEE Trans. Wireless Commun.

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airBP: Monitor Your Blood Pressure with Millimeter-Wave in the Air

Liang

Zhou

Wen

et al. 2023

ACM Trans. Internet Things

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Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff-based or newly-emerging wearable-based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this paper, we explore contact-less BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom-design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size COTS mmWave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with the mean error of -0.30 mmHg and -0.23 mmHg, as well as the standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.

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Scalable 3D Beam-Steering for Directional Millimeter Wave Wireless Networks

Cited by 2 publications

References 25 publications

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

airBP: Monitor Your Blood Pressure with Millimeter-Wave in the Air

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