TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a timevarying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy one-bit data are studied.By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit ℓ 1 -regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cramér-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.

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“…Before proceeding, we give several properties of the expression a ∈ S(b) which is used in the constraint (15).…”

Section: B ℓ 1 -Regularized Atomic-norm Minimization Methodsmentioning

confidence: 99%

“…In [14], the reduced time-on-target technique was introduced to perform compression in the Doppler domain. A recent work [15] extended the SUMMeR system to perform the temporal, spatial, and Doppler compression simultaneously.…”

mentioning

confidence: 99%

Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds

Xiang

Chen

et al. 2020

IEEE Trans. Signal Process.

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“…A tensor-based sub-Nyquist monostatic MIMO radar was proposed in [106] which used undersampled measurements in spectral, spatial, Doppler, and temporal domains to jointly estimate target AoAs, range, and Doppler. The received signals were modeled as a partial third-order tensor.…”

Section: A Tensor Techniques For Target Parameter Estimation In Mimo Radarmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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“…In the analog processing section of hybrid systems, it is common to employ phase shifters with constant modulus. Using analog switches, which are much simpler and cheaper than the phase shifters, it is possible to further make the overall system more energy-efficient by using subarrays of the larger full antenna array [16], [17]. Optimal selection of subarray elements reduces the power consumption of the analog phase shifters and low-noise amplifiers (LNAs) [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks

Elbir

Mishra

2020

IEEE Trans. Wireless Commun.

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146

116

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In millimeter wave communications, multiple-inputmultiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power is possible through use of subarrays. Unlike prior works which resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with an optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.Index Terms-Antenna selection, CNN, deep learning, hybrid beamforming, massive MIMO.A. M. E. is with the

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TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

Cited by 29 publications

References 31 publications

Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds

Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds

Tensor Decompositions in Wireless Communications and MIMO Radar

Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks

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