LTE Coverage prediction using different propagation models

Al-Hilfi, Haider Mohammed Turki; Daghal, Asaad S.

doi:10.1109/isscs52333.2021.9497392

Cited by 3 publications

(2 citation statements)

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“…Friis is determined by (1). "H tu and H ru are sender and receiver height correction factors in dB, respectively, and computed by ( 9) and (10).…”

Section: Okumura Modelmentioning

confidence: 99%

“…The revolutionary growth of wireless networks these days influences the development of wireless communication, especially in the quality of services (QoS) that manage telecommunication services' usability, performance, and reliability [1], [2]. Network planning plays a crucial key role in indoor and outdoor environments to obtain better QoS for wireless cellular networks [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

et al. 2023

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As demand for mobile wireless network services continues to rise, network planning and optimization significantly affect development. One of the critical elements in network planning is predicting pathloss. Thus, propagation models predict pathloss in indoor and outdoor environments. Choosing the appropriate propagation model for the area out of existing models is essential for network planning. Selected propagation models suitable with 2.5GHz, such as Friis Free Space Propagation Model (FSPL), Sandford University Interim (SUI), Ericsson, Okumura, and COST-231 HATA models, are utilized for evaluation and compared with empirical data collected from long-term evolution (LTE) networks in urban areas. The best acceptable model is chosen based on statistical results such as mean, standard deviation, and root mean square errors (RMSE). The analytical results show Cost-231 Hata model fits the empirical pathloss with a minimum RMSE of 5.27 dB.

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“…Friis is determined by (1). "H tu and H ru are sender and receiver height correction factors in dB, respectively, and computed by ( 9) and (10).…”

Section: Okumura Modelmentioning

confidence: 99%