An Effective Compressed-Sensing Inspired Deterministic Algorithm for Sparse Array Synthesis

Pinchera, Daniele; Migliore, Marco Donald; Schettino, Fulvio; Lucido, Mario; Panariello, G.

doi:10.1109/tap.2017.2767621

Cited by 61 publications

(43 citation statements)

References 45 publications

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“…Unfortunately, there are no known deterministic optimal techniques to solve the 0 -norm minimization, but as shown in [45,52,55,56], the 0 -norm minimization can be conveniently approximated in a computationally-efficient way by using a sequence of weighted 1 minimizations. We could then try to solve the following convex problem iteratively:…”

Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

“…As shown in [52], by means of a non-local, "smooth" weighting vector, it is possible to reduce the step of the radial discretization, preserving the small "clusters" that arise in the solution of the 1 -norm minimization. In particular, we have used:…”

Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

“…Once the algorithm has reached a satisfactory solution, it is very easy to extract the ring radii R p and excitations e p from vectorẽ, by means of the refined clustering approach described in [52].…”

Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

“…The aforementioned classification is obviously a very simplified one, since the years hybrid and mixed strategies have been presented [35][36][37][38][39][40][41]; furthermore, the recent development of compressive sensing [42], and related algorithms, has provided effective and computationally-efficient algorithms [43][44][45][46][47][48][49][50][51][52][53]. In particular, the synthesis of sparse linear arrays of hundreds of wavelengths was shown in [53], a result that to the best knowledge of the authors is currently unmatched.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

et al. 2019

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In this paper, we present an efficient technique for the synthesis of very large sparse arrays, with arbitrary circularly-symmetrical upper bounds for the pattern specifications. The algorithm, which is based on iterative smooth re-weighted L1 minimizations, is very flexible and is capable of achieving very good performances with respect to competitive algorithms. Furthermore, thanks to its efficiency, planar arrays of hundreds of wavelengths can be synthesized with limited computational effort.

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Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

Section: Smooth Re-weighted L1 Minimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

et al. 2019

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“…Sparse arrays can not only reduce the complexity of the feed networks but also can decrease the weight. Most studies on sparse antenna arrays [11][12][13] are focused on reducing the number of elements, the peak side lobe level, the computational effort, and so on but not considering the power transmission efficiency. In the MPT scenario, the element numbers of antenna arrays were reduced to 65% and 64% of the original one through compressive sensing (CS) and convex programming (CP) methods, respectively, in [14,15].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of the Sparse Uniform-Amplitude Concentric Ring Transmitting Array for Optimal Microwave Power Transmission

Zhou

Yang

Rahim

2018

International Journal of Antennas and Propagation

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Beam capture efficiency (BCE) is one key factor of the overall efficiency for a microwave power transmission (MPT) system, while sparsification of a large-scale transmitting array has a practical significance. If all elements of the transmitting array are excited uniformly, the fabrication, maintenance, and feed network design would be greatly simplified. This paper describes the synthesis method of the sparse uniform-amplitude transmitting array with concentric ring layout using particle swarm optimization (PSO) algorithm while keeping a higher BCE. Based on this method, uniform exciting strategy, reduced number of elements, and a higher BCE are achieved simultaneously for optimal MPT. The numerical results of the sparse uniform-amplitude concentric ring arrays (SUACRAs) optimized by the proposed method are compared with those of the random-located uniform-amplitude array (RLUAA) and the stepped-amplitude array (SAA), both being reported in the literatures for the maximum BCE. Compared to the RLUAA, the SUACRA saves 32% elements with a 1.1% higher BCE. While compared to the SAA, the SUACRA saves 29.1% elements with a bit higher BCE. The proposed SUACRAs have higher BCEs, simple array arrangement and feed network, and could be used as the transmitting array for a large-scale MPT system.

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Unconventional Array Architectures for Next Generation Wireless Communications

Anselmi,

Goudos,

Oliveri

et al. 2023

Sparse Arrays for Radar, Sonar, and Communications

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An Effective Compressed-Sensing Inspired Deterministic Algorithm for Sparse Array Synthesis

Cited by 61 publications

References 45 publications

Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

Synthesis of the Sparse Uniform-Amplitude Concentric Ring Transmitting Array for Optimal Microwave Power Transmission

Unconventional Array Architectures for Next Generation Wireless Communications

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