Low-rank physical model recovery from low-rank signal approximation

Hayes, Charles Ethan; McClellan, J.H.; Scott, Waymond R.

doi:10.1109/icassp.2017.7952733

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Many other applications are arising that require high dimensional parameter estimation. It has been shown that these problems can be solved efficiently in tensor form [20]- [23]. Of particular importance is the multilinear singular value decomposition (MSVD), which is a tensor analogue of the singular value decomposition (SVD) commonly seen in linear algebra.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Similar sparse estimation problems to (20) are posed in [20], [29], [30] where sparsity is an outcome of vector selection from an overcomplete dictionary. In these works sparsity is enforced in Q 1 by solving the optimization problem:…”

Section: Subspace Estimationmentioning

confidence: 99%

“…This is achieved by removing planes from Σ s corresponding with the weak singular values along with the interacting row, column, and fiber vectors. This acts as a denoising process [20] and the remaining reduced rank Tucker form is…”

Section: B Rank Reductionmentioning

confidence: 99%

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Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Ruble

Güvenç

2020

IEEE Access

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Fifth generation (5G) cellular standards are set to utilize millimeter wave (mmWave) frequencies, which enable data speeds greater than 10 Gbps and sub-centimeter localization accuracy. These capabilities rely on accurate estimates of the channel parameters, which we define as the angle of arrival, angle of departure, and path distance for each path between the transmitter and receiver. Estimating the channel parameters in a computationally efficient manner poses a challenge because it requires estimation of parameters from a high-dimensional measurement -particularly for multi-carrier systems since each subcarrier must be estimated separately. Additionally, channel parameter estimation must be able to handle hybrid beamforming, which uses a combination of digital and analog beamforming to reduce the number of required analog to digital converters. This paper introduces a channel parameter estimation technique based on the multilinear singular value decomposition (MSVD), a tensor analog of the singular value decomposition, for massive multiple input multiple output (MIMO) multi-carrier systems with hybrid beamforming. The MSVD tensor estimation approach provides a computationally efficient method and is shown to closely match the Cramer-Rao bound (CRB) of parameter estimates through simulations. Limitations of channel parameter estimation and communication waveform effects are also studied.

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Section: A Literature Reviewmentioning

confidence: 99%