Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks

Prabhu, M.; Kumar, B. Muthu; A., Ahilan

doi:10.1080/03772063.2023.2194279

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…The fuzzy strategy is commonly adopted for SMA variants. Prabhu M et al [77] proposed a fuzzy-based slime mould optimization for the carrier frequency offset (FSM-CFO). The fuzzy rules were designed and applied for the assignment of resource units (Rus) to a certain job.…”

Section: Fuzzymentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Fuzzymentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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“…. , j j c j B P A P P M G P (6) In which G j represents G j s DFT. The UEN sign makes use of a most probability detector, or MLD, to expect its very own alerts due to the fact it's miles given extra power.…”

Section: ^`mentioning

confidence: 99%

“…This enables more users to be served with the same amount of bandwidth, making it a viable solution for future wireless communication systems [5]. The ability of NOMA to serve a high number of users while sharing the same time and frequency resources is the primary reason for its implementation in 5G [6]. It provides excellent device performance, efficiency, expanded coverage, low latency, and massive networking [7,8].…”

Section: Introductionmentioning

confidence: 99%

Hexagonal Convolutional Neural Network for Noma Rician Channel Estimator using Hexagonal Quadrature Amplitude Modulation

2024

Informacije MIDEM

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NOMA techniques have attracted much attention due to their ability to support massive connectivity, heterogeneous data traffic, and ultra-low latency requirements, making them ideal for next-generation wireless communication networks. In this paper proposed a novel Hexa CNN for NOMA Rician channel estimator using Hexagonal Quadrature Amplitude Modulation for signal detection and channel estimation (Hexa-QAM). Conventional OFDM-NOMA with HQAM is utilized at the transmitter (tx) side as pilot symbols which inserted to OFDM-NOMA signals to employs the channel estimation (CE) and signal detection advantages of OFDM. Three pilot insertion types Comp, Block, and Hexa were used in the proposed model. The proposed Hexa-QAM can detect the symbols for all users without additional operations based on pilot responses and data signals. However, Hexa CNN is used at the receiver to accomplish a joint flexible signal detection. The Hexagonal Quadrature Amplitude Modulation (Hexa CNN) achieves much better error performance than similar detectors. The Energy Efficiency of the proposed Hexa-QAM technique is 17%, 18%, 23.2%, 23% and 28% better than existing techniques. The accuracy of the proposed technique can be as high as 99.95%, while that of traditional models like the OMA-NOMA, MRC, PD-NOMA and UR-NOMA is 84.9%, 87.58%, and 93.91%, respectively.

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“…Fuzzy strategy is commonly adopted in SMA variants. Prabhu, M et al [75] proposed a fuzzy based Slime Mould optimization for Carrier Frequency Offset (FSM-CFO) . The fuzzy rules were designed and applied for the assignment of Resource Units (RU) to a certain job.…”

Section: Fuzzymentioning

confidence: 99%

Advances in Slime Mould Algorithm: A comprehensive Survey

Wei,

Othman,

Daud

et al. 2023

Preprint

View full text Add to dashboard Cite

Slime Mould Algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime molds during foraging. Numerous researchers have widely applied SMA and its variants in various domains and proved its value by the experiments in literatures. In this paper a comprehensive survey on SMA is introduced, which is based on 130 articles visa Google-scholar between 2022 and July, 2023. Firstly, the theory of SMA is described. Secondly the improved SMA variants are provided and categorized according to the approach that they are applied with. Finally, it also discusses the main applications domains of SMA such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, image segmentation and etc. This review presents some research suggestion for researcher who is interested in this algorithm.

show abstract

Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks

Cited by 8 publications

References 18 publications

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A Comprehensive Survey

Hexagonal Convolutional Neural Network for Noma Rician Channel Estimator using Hexagonal Quadrature Amplitude Modulation

Advances in Slime Mould Algorithm: A comprehensive Survey

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