Propagation Model Optimization based on Particles Swarm Optimization and Genetic Algorithm Cross Implementation Application to Yaoundé Town

Michel, Deussom Djomadji Eric; Basile, Kabiena Ivan; Tonyé, Emmanuel

doi:10.5120/ijca2023922843

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of this study is to integrate the use of the DE algorithm in the resolution of a real problem in the field of telecommunications, which is optimizing propagation models. Based on the hypothesis that the standard propagation models currently implemented in Cameroon have been developed in other countries and therefore do not accurately reflect the characteristics of the [21] for Propagation Model Optimization. Deussom Eric and Tonye Emmanuel have also proposed other methods for propagation model optimization in [22], in [23] the same authors proposed a solution for propagation model optimization based on GA; in [24] the same authors used Newton second order algorithm to optimize propagation model and linear regression in [25].…”

Section: Introductionmentioning

confidence: 99%

Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua

Djomadji,

Kabiena,

Mandengue

et al. 2023

JCC

View full text Add to dashboard Cite

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem; the newly obtained models can be used for radio planning in the How to cite this paper:

show abstract

Section: Introductionmentioning

confidence: 99%