Performance Evaluation of Channel Modeling and Path Loss Measurements for Wireless Communication Systems in Urban and Rural Territories. (Dept. E. Electronics)

Zakaria, Yahia; Hamad, Ehab K. I.; El-Hamid, Ahmed S. Abd; El–Khatib, K.M.

doi:10.21608/bfemu.2022.215693

Cited by 2 publications

(2 citation statements)

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“…Performance metrics for path loss models are essential in evaluating the accuracy and effectiveness of models used to predict signal propagation and attenuation in wireless communication systems [21,35,50,51]. These models play a critical role in various applications, such as network planning, resource allocation, and the optimization of wireless communication systems.…”

Section: Performance Metrics For Path Loss Modelsmentioning

confidence: 99%

“…These models play a critical role in various applications, such as network planning, resource allocation, and the optimization of wireless communication systems. Some common performance metrics for path loss models include, e.g., mean square error (MSE), mean absolute error (MAE), mean error (ME), and R2 score [50,51].…”

Section: Performance Metrics For Path Loss Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

Shaibu,

Onwuka,

Salawu

et al. 2023

Future Internet

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The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.

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Section: Performance Metrics For Path Loss Modelsmentioning

confidence: 99%

Section: Performance Metrics For Path Loss Modelsmentioning

confidence: 99%