Optimization of propagation path loss model in 4G wireless communication systems

Sharma, Purnima K.; Sharma, Dinesh; Sai, Thota Vijay

doi:10.1109/icisc.2018.8399004

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…The forecast employing test mobile system (TEMS) research and discovery network planning tools provided the best prediction with the Ericsson model among the models used. An optimized cost 231 model was developed Sharma et al [15] to estimate path-loss in Jaipur, India over a 4G wireless communication link operating at 1,800 MHz. The measurement supports the distinction between anticipated and measured path-loss using the measured field strength as basis.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of a modified propagation model of a wireless mobile communication system in a 4G network

Olukunle,

Kunle,

Joel

et al. 2023

IJECE

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<span lang="EN-US">Pathloss is a key element that causes signal deterioration in the channel as the signal power reduces inversely with propagation distance, this deterioration experienced by the channel is majorly as a result of reflection, absorption, and scattering of the signal. This study however takes into consideration the radio path loss for precise base station (BS), frequency, and power adjustment prediction evaluated over a frequency of 2.3 GHz. With a distance range between 0.1 and 1.5 km for collection of data on the measured received signal strength (MRSS), five empirical models and a modified model were used to validate the measured data to determine their suitability for pathloss prediction at Federal University of Technology, Owerri (FUTO), Imo state, Nigeria. The results shows that the root mean square error (RMSE) for the Okumura-Hata, COST 231-Hata, Ericsson model, Lee, Stanford University Interim (SUI), ECC-33, and modified models are 14.33, 9.73, 25.79, 48.4, 33.76, and 8.31 dB respectively. Additionally, the Ericsson model provided 0.498 dB, the COST 231-Hata recorded 0.733 dB, and the modified model provided 0.453 dB for mean absolute percentage error (MAPE). Therefore, the improved model produces the best results, consequently, be deployed to approximately predict path loss for mobile radio coverage in Owerri, Nigeria.</span>

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Section: Literature Reviewmentioning

confidence: 99%

“…where d 0 is the distance between transmitter and receiver, and 𝜆 is the wavelength. The path loss exponent v is given by (15).…”

Section: Sui Modelmentioning

confidence: 99%

Development of a modified propagation model of a wireless mobile communication system in a 4G network

Olukunle,

Kunle,

Joel

et al. 2023

IJECE

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“…Es válido para alturas, de la EM entre 2 m y 3 m y de la EB entre 10m y 80m. El rango de frecuencia para el modelo va desde los 0 MHz hasta los 2000 MHz [17].…”

Section: B Modelo Walfisch -Ikegamiunclassified

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Barrios-Ulloa

2021

CESTA

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Tres modelos de propagación empíricos fueron comparados: Cost 231 Hata, Walfisch Ikegami (WI) e Interino de la Universidad de Stanford (SUI); para determinar su efectividad en un entorno urbano. Se hicieron mediciones en la banda de los 1900 MHz en un sector de la ciudad de Barranquilla, Colombia; y la evaluación se hizo mediante comparación de valores de error de predicción y relativo de cada modelo con respecto a las mediciones. Los resultados muestran que el modelo WI presentó el menor error relativo en comparación con el Cost 231 Hata y SUI tipo B. Los niveles altos de error indican que los modelos comparados no representan adecuadamente las mediciones en el escenario considerado.

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“…All these models can be used over an extensive range of relevant frequencies to predict path loss. [40,41].…”

Section: Path Loss Modelsmentioning

confidence: 99%

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

2020

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In a network topology, where 5G (mm Waves) have better coverage footprint compared to 4G (LTE or LTE-A) technology, mobile devices would generally be handed over from 4G to 5G. In this work, a supervised intelligent prediction technique for improved handover success rate (HSR) from 4G to 5G technology is proposed. The technique is applicable for base stations enabled with sub-6-GHz and mm-wave bands. This technique is novel since it can predict HSR even before switching to 5G radio circuitry or initiating its measurement gap for acquisition of mm-wave reference signal received power (RSRP) unlike conventional algorithms. Thus, preempting all handovers which are likely to fail will provide improvements in latency, delay, and handover success rate, as well as decrease call drops. Therefore, this research work answers previous research shortcomings and can unleash applications of supervised intelligent algorithms for predicting the HSR from 4G to 5G. The proposed algorithm is validated by showing improvements obtained through simulation results performed using Python-based framework. The proposed algorithm is tested for reliability with increasing parameters such as the intensity number of UEs and simulation time. Improvements in standard handover algorithm are also proposed.

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Optimization of propagation path loss model in 4G wireless communication systems

Cited by 12 publications

References 5 publications

Development of a modified propagation model of a wireless mobile communication system in a 4G network

Development of a modified propagation model of a wireless mobile communication system in a 4G network

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

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