An Introduction to Compressed Sensing

Koep, Niklas; Behboodi, Arash; Mathar, Rudolf

doi:10.1007/978-3-319-73074-5_1

Cited by 3 publications

(2 citation statements)

References 82 publications

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“…The problem of DOA estimation is to obtain the incident angles of the sources aided by the array output signal vector and geometry. Generally, the number of sources is finite, and the signal arrival directions are sparsely distributed in space [ 19 ]. Hence, the spatial domain

can be divided into N sets of discrete angles

with equal spacing:

…”

Section: Signal Modelmentioning

confidence: 99%

DOA Estimation Method Based on Improved Deep Convolutional Neural Network

Zhao

Zhan

et al. 2022

Sensors

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For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.

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can be divided into N sets of discrete angles

with equal spacing:

…”

Section: Signal Modelmentioning

confidence: 99%

DOA Estimation Method Based on Improved Deep Convolutional Neural Network

Zhao

Zhan

et al. 2022

Sensors

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show abstract

“…

can be expanded by:

where

is an 1-sparse vector with all-zeros elements except for position

, and

is an L -sparse vector, assuming no resource collision among users. By sending

to the support recovery algorithm [ 29 ], the column index set of

, as we modeled, the support set

, is searched out. Remap the elements in

back to decimals

by aligning the

-th column to

.…”

Section: Joint Intra/inter-slot Coding Schemementioning

confidence: 99%

Joint Intra/Inter-Slot Code Design for Unsourced Multiple Access in 6G Internet of Things

Niu

Dong

et al. 2022

Sensors

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Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding scheme based on the T-Fold IRSA (irregular repetition slotted Aloha) paradigm by using joint Intra/inter-slot code design and optimization. Our scheme adopts interleave-division multiple access (IDMA) to enhance the intra-slot coding gain and the low-complexity joint intra/inter-slot SIC (successive interference cancellation) decoder structure to recover multi-user payloads. Based on the error event decomposition and density evolution analysis, we build a joint intra/inter-slot coding parameter optimization algorithm to minimize the SNR (signal-to-noise ratio) requirement at an expected system packet loss rate. Numerical results indicate that the proposed scheme achieves energy efficiency gain by balancing the intra/inter-slot coding gain while maintaining relatively low implementation complexity.

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