A Fibonacci Branch Search (FBS)-Based Optimization Algorithm for Enhanced Nulling Level Control Adaptive Beamforming Techinique

Zhang, Haichuan; Zeng, Fangling

doi:10.1109/access.2019.2949028

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…The estimation techniques make use of linear co-efficient by making use of singular value decomposition and the processing is performed on each of the individual co-efficient are applied to elements [44].…”

Section: Background For Radiation Formationmentioning

confidence: 99%

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

Thazeen¹,

Mallikarjunaswamy²,

Siddesh³

et al. 2021

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Background/objectives: The objective of the study is to increase the resolution and radiate sharper beam towards the user in mobile communication using Smart Antenna. Methodology: The Conventional and Subspace Algorithms from the literature are studied and simulated in MATLAB so that the foundation is laid for better detection of algorithms and radiation formation. The results are explained for varying number of antenna elements and mobiles sources placed close to far. Findings: The classical direction of arrival algorithms namely CAPCON, Maximum Entropy Method, Maximum Likelihood Method are used to find the direction of mobile users based on the computation of the power spectrum. Several methods namely Least Mean Square, Griffiths Method, Variable Step Size Griffiths and Recursive Least Square are used to form the main beam for the user detected by the direction of arrival algorithms. Novelty/improvements: In order to take further the research on enhancing the resolution and having a higher convergence rate with reasonable step size, this paper presents the well-known conventional and modern algorithms from the literature. The results are simulated are well described for performing parameters.

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Section: Background For Radiation Formationmentioning

confidence: 99%

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

Thazeen¹,

Mallikarjunaswamy²,

Siddesh³

et al. 2021

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“…The Fibonacci tree optimization algorithm (FTO) [ 44 ] is a sophisticated optimization algorithm. It resolves the optimal solution of the problem by alternating iterations of global scanning and local scanning.…”

Section: Related Workmentioning

confidence: 99%

“…Recall that one-dimensional Fibonacci (golden ratio) search cannot proficiently take care of multi-variate issues, giving rise to Fibonacci Tree Optimization—an improved multi-dimensional search algorithm [ 44 ].…”

Section: Fibonacci Tree Optimization Algorithmmentioning

confidence: 99%

Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm

Cai

2021

Sensors

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Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.

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“…EM-driven design, otherwise dictated by reliability, entails considerable computational costs, even in the case of local optimization. Further, the lack of good initial designs but also multimodality of the functional landscapes pertinent to many problems (e.g., pattern synthesis in antenna arrays 11 ) call for global search, the cost of which may be considerably higher 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

Low-cost quasi-global optimization of expensive electromagnetic simulation models by inverse surrogates and response features

Kozieł

Pietrenko‐Dabrowska

2022

Sci Rep

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Conceptual design of contemporary high-frequency structures is typically followed by a careful tuning of their parameters, predominantly the geometry ones. The process aims at improving the relevant performance figures, and may be quite expensive. The reason is that conventional design methods, e.g., based on analytical or equivalent network models, often only yield rough initial designs. This is especially the case for miniaturized components featuring considerable electromagnetic (EM) cross couplings, or antenna systems with non-negligible radiator coupling (e.g., MIMO, closely-spaced arrays). For reliability reasons, parametric optimization is carried out using EM simulation tools, which is a time-consuming task. In many cases, designer needs to resort to a global search, especially when handling several objectives and constraints is necessary, or the high-frequency structure under design is overly complex. Combination of both aforementioned factors makes it no longer possible to rely on engineering insight, even to detect a promising region of the design space. Unfortunately, nature-inspired algorithms, commonly employed for solving these tasks typically exhibit significant computational expenditures. This paper proposes a simple yet efficient method for globalized search using a response feature approach and inverse regression surrogates. Owing to less nonlinear dependence of the feature point coordinates on the system variables (as compared to the original responses, e.g., S-parameter frequency characteristics), our methodology permits a rapid identification of the most appropriate regions of the parametric space, and further design tuning by means of local routines. At the same time, the overall optimization cost is comparable to the cost of local procedures. The proposed approach is validated using several high-frequency structures (a dual-band antenna, a microstrip coupler, an impedance matching transformer) optimized under different design scenarios. Global search capability and computational efficiency are demonstrated through comprehensive comparisons with multiple-start local search, as well as particle swarm optimizer, a representative nature-inspired algorithm.

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A Fibonacci Branch Search (FBS)-Based Optimization Algorithm for Enhanced Nulling Level Control Adaptive Beamforming Techinique

Cited by 7 publications

References 30 publications

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm

Low-cost quasi-global optimization of expensive electromagnetic simulation models by inverse surrogates and response features

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