mmWave Path loss Prediction Model Using Grey System Theory for Urban Areas

Phaiboon, Supachai; Phokharatkul, Pisit

doi:10.1109/icramet51080.2020.9298608

Cited by 4 publications

(2 citation statements)

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“…Considering the deployment of empirical models for path loss prediction in indoor environments in the mid-band frequency spectrum, quite a lot of the proposed models have outperformed the conventional ones, like in the case of [70], where the proposed model outperformed the log-distance model with the minimum value of RMSE at 2.1 GHz. On the contrary, the ITU-R model was overestimated at 3.5 GHz in [61] with a MAPE value of 10.71, while FSPL was underestimated with a MAPE value of 4.83, as shown in Figure 5. On the high-band spectrum, CI and FI models performed excellently well in the work of [48] at 14, 18, and 22 GHz, and [71] at 28 GHz.…”

Section: Performance Evaluation Of Empirical Path Loss Models In Indo...mentioning

confidence: 94%

“…Numerous studies have sought optimal path loss models for signal attenuation prediction in 5G communication scenarios [60], especially in mid-band and high-band frequencies. A case in point is the work of [61], which devised a model for indoor environments at 3.5 GHz. Using two omnidirectional antennas in building measurements, this research highlighted the utility of path loss parameters for describing coverage and large-scale fading in propagation channels.…”

Section: Reviewed Papers On Empirical-based Path Loss Modelsmentioning

confidence: 99%

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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 Evaluation Of Empirical Path Loss Models In Indo...mentioning

confidence: 94%

Section: Reviewed Papers On Empirical-based Path Loss Modelsmentioning

confidence: 99%