Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

Hinga, Simon Karanja; Atayero, Aderemi A.

doi:10.1109/access.2021.3114771

Cited by 12 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where α and γ, β, and P ABG L ( f , d) are coefficients illustrating the dependence of path loss on distance and frequency, respectively; the optimal offset value for path loss in dB; and the path loss in dB over frequency and distance, respectively. The 3D transmitter-receiver (T-R) separation distance is denoted as d in meters, f is the carrier frequency in GHz, and W ABG σ is the SF standard deviation representing large-scale signal fluctuations regarding the mean path loss over distance [46]. D. FI Model [46] In this model, the intercept parameter can vary based on the environment or specific scenario to present a more accurate estimation of the path loss [47].…”

Section: Current Empirical Modelsmentioning

confidence: 99%

“…The 3D transmitter-receiver (T-R) separation distance is denoted as d in meters, f is the carrier frequency in GHz, and W ABG σ is the SF standard deviation representing large-scale signal fluctuations regarding the mean path loss over distance [46]. D. FI Model [46] In this model, the intercept parameter can vary based on the environment or specific scenario to present a more accurate estimation of the path loss [47]. Traditional path loss models assume a fixed intercept value for a particular environment.…”

Section: Current Empirical 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

View full text Add to dashboard Cite

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.

show abstract

Section: Current Empirical Modelsmentioning

confidence: 99%