Hybrid Beamforming for Active Sensing Using Sparse Arrays

Rajamäki, Robin; Chepuri, Sundeep Prabhakar; Koivunen, Visa

doi:10.1109/tsp.2020.3032657

Cited by 29 publications

(9 citation statements)

References 29 publications

(63 reference statements)

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“…As shown in Fig. 1, each array element is con- 1 We address the more general case of hybrid beamforming with quantized phase shifts in the longer journal version of this paper [14]. nected to a single RF-IF front-end and DAC or ADC via a phase shifter.…”

Section: Signal Model and Definitionsmentioning

confidence: 99%

“…We note that the fully-digital ULA achieves the desired PSF using one component image, whereas the fully-digital MRA requires two components. 2 2 Using an alternating minimization algorithm [14] with tolerance εmax. By Theorem 1, the fully-analog beamformers then exactly achieve the fully-digital PSFs using Q = 4 (ULA), respectively Q = 8 (MRA) component images.…”

Section: Numerical Examplesmentioning

confidence: 99%

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Analog Beamforming For Active Imaging Using Sparse Arrays

Rajamäki

Chepuri

Koivunen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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This paper studies hybrid beamforming for active sensing applications, such as millimeter-wave or ultrasound imaging. Hybrid beamforming can substantially lower the cost and power consumption of fully digital sensor arrays by reducing the number of active front ends. Sparse arrays can be used to further reduce hardware costs. We consider phased arrays and employ linear beamforming with possibly sparse sensor configurations at both the transmitter and receiver. The quality of the acquired images is improved by adding together several component images corresponding to different transmissions and receptions. In order to limit the acquisition time of the image, we formulate an optimization problem for minimizing the number of component images, subject to achieving a desired point spread function.Since this problem is not convex, we propose algorithms for finding approximate solutions in the fully digital beamforming case as well as in the more challenging hybrid and analog beamforming cases that employ quantized phase shifters. We also derive upper bounds on the number of component images needed for achieving the fully digital solution using certain hybrid architectures, and give the beamforming weights in closed form for these cases. Simulations demonstrate that a hybrid sparse array with very few elements, and even fewer front ends, can achieve the resolution of a fully digital uniform array, at the expense of longer image acquisition time and lower array gain.

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Section: Signal Model and Definitionsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Analog Beamforming For Active Imaging Using Sparse Arrays

Rajamäki

Chepuri

Koivunen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

Self Cite

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“…Therefore, we can sample the ULA's NAF response with frequency B N , obtaining LpnB ´1 N q. One can apply (9) to get the reconstructed NAF response…”

Section: A Angular Response Signal Sampling and Reconstructionmentioning

confidence: 99%

“…However, in this work we show that this leads to distortion in angular response reconstruction. Recent work on radio imaging with arrays [9] performs acquisitions with fine sampling of the angular domain, generating many (redundant) acquisitions with large overhead and reducing the amount of radio resources available for ISAC. Moreover, long acquisition time in dynamic scenarios would cause signal change during the acquisitions, distorting the desired image, as will be discussed later.…”

Section: Introductionmentioning

confidence: 99%

Sampling and Reconstructing Angular Domains with Uniform Arrays

Mandelli¹,

Marcus²,

Du³

2022

Preprint

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The surge of massive antenna arrays in wireless networks calls for the adoption of analog/hybrid array solutions, where multiple antenna elements are driven by a common radio front end to form a beam along a specific angle in order to maximize the beamforming gain. Many heuristics have been proposed to sample the angular domain by trading off between sampling overhead and angular scanning step size, where arbitrarily small angular step size is only attainable with infinite sampling overhead. In this work we show that, for uniform linear and rectangular arrays, loss-less reconstruction of the array's angular response at arbitrary angular precision is possible using finite number of samples without resorting to assumptions of angular sparsity. The proposed method, sampling and reconstructing angular domain (SARA), defines how many and which angles to be sampled and the corresponding reconstruction. This general solution to scan the angular domain can therefore be applied not only to beam acquisition and channel estimation, but also to radio imaging techniques, making it a candidate for future integrated sensing and communications (ISAC). We evaluate our proposal by numerical evaluations, which provide clear advantages versus the other considered baselines both in terms of angular reconstruction performance and computational complexity.

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“…However, the cost of the hardware increases along with the number of antenna elements. One possible solution to reduce costs without sacrificing the angle resolution is by utilizing MIMO radar [37][38][39]. For example, the MIMO radar with two-dimensional sparse arrays and hundreds of virtual elements can enable high-fidelity four-dimensional sensing (range, Doppler, azimuth and elevation) [40].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Zhang

Geng

Lin

2021

Sensors

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In this paper, comprehensive mPoint, a method for generating 3D (range, azimuth, and elevation) point cloud of human targets using a Frequency-Modulated Continuous Wave (FMCW) signal and Multi-Input Multi-Output (MIMO) millimeter wave radar is proposed. Distinct from the TI-mPoint method proposed by TI technology, a comprehensive mPoint method considering both the static and dynamic characteristics of radar reflected signals is utilized to generate a high precision point cloud, resulting in more comprehensive information of the target being detected. The radar possessing 60–64 GHz FMCW signal with two sets of different dimensional antennas is utilized in order to experimentally verify the results of the methodology. By using the proposed process, the point cloud data of human targets can be obtained based on six different postures of the underlying human body. The human posture cube and point cloud accuracy rates are defined in the paper in order to quantitively and qualitatively evaluate the quality of the generated point cloud. Benefitting from the proposed comprehensive mPoint, evidence shows that the point number and the accuracy rate of the generated point cloud compared with those from the popular TI-mPoint can be largely increased by 86% and 42%, respectively. In addition, the noise level of multipath reflection can be effectively reduced. Moreover, the length of the algorithm running time is only 1.6% longer than that of the previous method as a slight tradeoff.

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Hybrid Beamforming for Active Sensing Using Sparse Arrays

Cited by 29 publications

References 29 publications

Analog Beamforming For Active Imaging Using Sparse Arrays

Analog Beamforming For Active Imaging Using Sparse Arrays

Sampling and Reconstructing Angular Domains with Uniform Arrays

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

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