Differential Evolution in Wireless Communications:  A Review

Okagbue, Hilary I.; Adamu, Muminu O.; Anake, T. A.

doi:10.3991/ijoe.v15i11.10651

Cited by 12 publications

(5 citation statements)

References 160 publications

(159 reference statements)

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“…Presentation of the detailed history, characteristics strengths, variants and weaknesses of DE are made in [4]. Identification of seven broad areas are made as different domains of application of DE in wireless communications it was observed that the two major areas where the DE is applied are the coverage area maximization and energy consumption minimization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Optimization is a very important topic because it helps to find the best solution from the number of solutions. Hence optimized chaotic code plays a very important role in wireless communication techniques [3], [4]. Considering optimization, Differential evolution (DE) is an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover, etc.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Spreading Code Using Modified Differential Evolution for Wireless Communication

Kashyap

Pathak

Yadav

2021

Wireless Pers Commun

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Spread spectrum linked to optimization techniques play a very important role in today's world. Optimization gives the best one among all the solutions and the spread spectrum is used to minimize distortion, hence optimized spreading code is very important in wireless communication. This study shows a comparison of two recent modified techniques of DE with the help of engineering design problems and application of that modified DE (considering the better one) in wireless communication to optimize logistic map based spreading code. A comparative study of properties of both optimized and non-optimized spreading code is also discussed in this paper. The performance of the optimized spreading code is also evaluated with the help of bit error rate (BER) by applying it in static and dynamic direct sequence spread spectrum system (DSSS). The performance of the proposed technique (optimized dynamic logistic map code based DSSS) displays better results than non-optimized spreading code and orthogonal spreading code considering BER. Therefore, the proposed method can be efficiently applied in the wireless communication system.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Spreading Code Using Modified Differential Evolution for Wireless Communication

Kashyap

Pathak

Yadav

2021

Wireless Pers Commun

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“…DE is applied as clustering algorithm in order to ensure minimal energy consumption and network resilience and sustainability [37]. Other applications can be seen in [38][39][40].…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

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“…However, they generate results from the example that they process. ANN is a member of evolutionary computational methods [9].…”

Section: Introductionmentioning

confidence: 99%

Review on the Application of Artificial Neural Networks in Real Estate Valuation

Nkolika¹

2020

IJATCSE

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Real estate appraisal is needed in assessment of the value of properties and contribute the regional economy of any country. Real estate valuation is thus an important subject, which has to be studied carefully as there are many individual subjective criteria, which often results in variations, which makes the traditional valuation methods to be inadequate. This work aimed at coping with the complexity of real estate valuation, by putting forth the advantages of artificial neural network, one of the novel model of real estate appraisal. The review of literature has helped to establish that the neural network model is better than the traditional model previously. This work opens up a new and better understanding to the principle of real estate evaluation.

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Differential Evolution in Wireless Communications: A Review

Cited by 12 publications

References 160 publications

Optimization of Spreading Code Using Modified Differential Evolution for Wireless Communication

Optimization of Spreading Code Using Modified Differential Evolution for Wireless Communication

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Review on the Application of Artificial Neural Networks in Real Estate Valuation

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