Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Mohapatra, Pradyumna Kumar; Rout, Saroja Kumar; Bisoy, Sukant Kishoro; Sain, Mangal

doi:10.1109/access.2022.3174369

Cited by 17 publications

(5 citation statements)

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“…applications, including remote monitoring, performance control, and infrastructure management [1,2,3,4], is at the forefront of accomplishing these objectives. Recently, Cloud Computing [5,6,7] has gained popularity as a new enterprise model for providing on-demand services to users as needed.…”

Section: Eai Endorsed Transactions On Scalable Information Systemsmentioning

confidence: 99%

A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

Rout¹,

Ravindra²,

Meda³

et al. 2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Cloud services are becoming increasingly important as advanced technology changes. In these kinds of cases, the volume of work on the corresponding server in public real-time data virtualized environment can vary based on the user’s needs. Cloud computing is the most recent technology that provides on-demand access to computer resources without the user’s direct interference. Consequently, cloud-based businesses must be scalable to succeed.OBJECTIVES: The purpose of this research work is to describe a new virtual cluster architecture that allows cloud applications to scale dynamically within the virtualization of cloud computing scale Using auto-scaling, resources can be dynamically adjusted to meet multiple demands. METHODS: An auto-scaling algorithm based on the current implementation sessions will be initiated for automated provisioning and balancing of virtualized resources. The suggested methodology also considers the cost of energy.RESULTS: The proposed research work has shown that the suggested technique can handle sudden load demands while maintaining higher resource usage and lowering energy costs efficiently.CONCLUSION: Auto-scaling features are available in measures in order groups, allowing you to automatically add or remove instances from a managed instance group based on changes in load. This research work provides an analysis of auto-scaling mechanisms in cloud services that can be used to find the most efficient and optimal solution in practice and to manage cloud services efficiently.

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Section: Eai Endorsed Transactions On Scalable Information Systemsmentioning

confidence: 99%

A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

Rout¹,

Ravindra²,

Meda³

et al. 2023

ICST Transactions on Scalable Information Systems

Self Cite

View full text Add to dashboard Cite

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“…This equalizer avoids introducing any phase ambiguity and does not get trapped in local optima. A novel training strategy using the Fuzzy Firefly Algorithm is proposed for channel equalization (Mohapatra et al, 2022). By employing an appropriate network topology and parameters, the suggested training system exhibits enhanced exploration and exploitation capabilities, as well as the ability to address local minima issues.…”

Section: Introductionmentioning

confidence: 99%

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

Zhou,

Zhang,

et al. 2024

Front. Mar. Sci.

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IntroductionIn shallow-water environments, the reliability of underwater communication links is often compromised by significant multipath effects. Some equalization techniques such as decision feedback equalizer, and deep neural network equalizer suffer from slow convergence and high computational complexity.MethodsTo address this challenge, this paper proposes a simplified decision feedback Chebyshev function link neural network equalizer (SDF-CFLNNE). The structure of the SDF-CFLNNE employs Chebyshev polynomial function expansion modules to directly and non-linearly transform the input signals into the output layer, without the inclusion of hidden layers. Additionally, it feeds the decision signal back to the input layer rather than the function expansion module, which significantly reduces computational complexity. Considering that, in the training phase of neural networks, the random initialization of weights and biases can substantially impact the training process and the ultimate performance of the network, this paper proposes a chaotic sparrow search algorithm combining the osprey optimization algorithm and Cauchy mutation (OCCSSA) to optimize the initial weights and thresholds of the proposed equalizer. The OCCSSA utilizes the Piecewise chaotic population initialization and combines the exploration strategy of the ospreywith the Cauchy mutation strategy to enhance both global and local search capabilities. RseultsSimulations were conducted using underwater multipath signals generated by the Bellhop Acoustic Toolbox. The results demonstrate that the performance of the SDFCFLNNE initialized by OCCSSA surpasses that of CFLNN-based and traditional nonlinear equalizers, with a notable improvement of 2-6 dB in terms of signal-to-noise ratio at a bit error rate (BER) of 10−4 and a reduced mean square error (MSE). Furthermore, the effectiveness of the proposed equalizer was validated using the lake experimental data, demonstrating lower BER and MSE with improved stability. DiscussionThis underscores thepromise of employing the SDFCFLNNE initialized by OCCSSA as a promising solution to enhance the robustness of underwater communication in challenging environments.

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“…For the problem of channel equalization, Ingle et al 41,42 presented some effective methods. Training of neural network with FFA and Bat algorithms and its modified form in nonlinear channel equalization has been well reported by Mohapatra et al 43,44 Radial Basis Function Neural Networks (RBFNN), on the other hand, is identical to the optimal Bayesian equalizer 6 and will find global minima 7 if it is properly implemented. The literature also proves that RBFNNs perform better than ANNs despite their simpler complexity.…”

Section: Introductionmentioning

confidence: 99%

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

Mohapatra¹,

Rout²,

Nayak

2023

Adaptive Control & Signal

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The equalization of digital channels is generally known to be a nonlinear classification problem. Applications such as these can benefit from networks that approximate nonlinear mappings. It gets good performance by adjusting only one coefficient and one center closest to the input vector of the radial basis function network (RBFN), which is a simplified version of stochastic gradient techniques. Artificial Neural Networks (ANNs) are suitable for channel equalization because they have the capability to map between variables. Having only one hidden layer in Radial Basis Function Neural Network (RBFNN) makes it the most preferred equalizer to mitigate distortions in the channels. The ability to equalize nonlinear channels is strength of radial RBFNN, which is a simplified version of stochastic gradient techniques. The conventional “hit and trial” approach poses the greatest difficulty when designing RBFNN Equalizers. As a solution to these limitations, this work suggests a training strategy based on Gaussian distribution estimation strategy (GDE) in hybrid butterfly optimization algorithm (GDEBOA) for RBFNN channel equalizer. The proposed RBFNN equalizer is being trained using a population‐based optimization algorithm. An algorithm, called GDEBOA, based on a GDE approach is developed in this paper. The proposed training scheme significantly outperforms existing metaheuristic algorithms in terms of mean square error (MSE) and bit error rate (BER). Furthermore, based on burst error scenarios and bit error probability (BEP), the proposed method has demonstrated greater robustness than other methods when faced with such scenarios. The effectiveness of the suggested scheme over a wide range of signal‐to‐noise ratios has been validated by several simulation studies. Additionally, statistical importance of the suggested technique is analyzed. It is visualized that GDEBOA performs better than existing algorithms for training RBFNN equalizer.

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Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Cited by 17 publications

References 50 publications

A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

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