A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Zhang, Hanfei; Xiao, Shungen; Ping, Zhou

doi:10.3390/sym12020231

Cited by 7 publications

(8 citation statements)

References 26 publications

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“…Zhang et al reported "A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting" [6]. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting.…”

Section: Lan Et Al Reported "Symmetric Modeling Of Communication Effectiveness and Satisfactionmentioning

confidence: 99%

Selected Papers from IIKII 2019 Conferences in Symmetry

Meen

Tijus

2020

Symmetry

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The International Institute of Knowledge Innovation and Invention (IIKII) is an institute that promotes the exchange of innovations and inventions, and establishes a communication platform for international innovations and researches. In 2019, IIKII cooperated with the Institute of Electrical and Electronics Engineers (IEEE) Tainan Section Sensors Council to hold IEEE conferences such as IEEE ICIASE 2019, IEEE ECBIOS 2019, IEEE ICKII 2019, ICUSA-GAME 2019, and IEEE ECICE 2019. This Special Issue entitled “Selected Papers from IIKII 2019 conferences” aims to select excellent papers from IIKII 2019 conferences, including symmetry in physics, chemistry, biology, mathematics, and computer science, etc. It selected 21 excellent papers from 750 papers presented in IIKII 2019 conferences on the topic of symmetry. The main goals of this Special Issue are to encourage scientists to publish their experimental and theoretical results in as much detail as possible, and to discover new scientific knowledge relevant to the topic of symmetry.

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Section: Lan Et Al Reported "Symmetric Modeling Of Communication Effectiveness and Satisfactionmentioning

confidence: 99%

Selected Papers from IIKII 2019 Conferences in Symmetry

Meen

Tijus

2020

Symmetry

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“…CS-based greedy pursuit algorithms adopted in CS include OMP [15][16][17], StOMP [18], ROMP [19,20], SP [21], CoSaMP [22], SAMP [23], EAMP [24], and IGSAMP [26]. Utilizing some criteria, they can approximate the sparse signals iteratively.…”

Section: A Review Of the Greedy Pursuit Algorithmsmentioning

confidence: 99%

“…The third category includes a set of greedy pursuit algorithms, which are to easy implement and have low computational complexity [13][14][15][16][17][18][19][20][21]. Specifically, orthogonal matching pursuit (OMP) [15][16][17], stage-wise OMP (StOMP) [18], and regularized orthogonal matching pursuit (ROMP) [19,20] have been proposed. The reconstruction complexity of basic greedy pursuit algorithms is roughly about O(kMN), which is much lower than that of BP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

et al. 2021

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Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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“…Many difficult real-time engineering problems have been solved by using heuristic algorithms inspired by the behavior of many events or creatures in nature [18][19][20][21][22][23][24][25][26][27][28][29][30]. With the help of heuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution algorithm (DE), ML estimation is computed recursively for optimizing subspace search.…”

Section: Introductionmentioning

confidence: 99%

“…BSA algorithm is a new evolutionary algorithm that provides more efficient solutions with fewer parameters and has recently been used in wide application areas [22]. some studies are using BSA in many areas such as antenna design, medicine, meteorology, Complementary metal oxide semiconductor (CMOS) circuit design, and chemistry [22][23][24][25][26][27][28][29]. When we consider the literature, there is no heuristic-based proposal to eliminate the disadvantages of ML algorithms in MIMO-NOMA systems.…”

Section: Introductionmentioning

confidence: 99%

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Seyman¹

2022

Computer Systems Science and Engineering

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One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.

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A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Cited by 7 publications

References 26 publications

Selected Papers from IIKII 2019 Conferences in Symmetry

Selected Papers from IIKII 2019 Conferences in Symmetry

Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

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