Path Loss Prediction in Urban Areas: A Machine Learning Approach

Rafie, Irfan Farhan Mohamad; Lim, Soo Yong; Chung, Michael Jenn Hwan

doi:10.1109/lawp.2022.3225792

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…and the major features of the industrial scenario, which is instead the main issue tackled in this work. According to an assessment framework already experienced in previous investigations [36], [38], RT simulations at different frequencies and on different realizations of the industrial environment are leveraged to gather the data required to train and test artificial neural networks (ANN) to learn the dependence of PL, shadowing, and DS on input features like the communication frequency and the machine density. The trained ML networks are then effectively used to complement and further extend the propagation data achieved from RT.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment

Zadeh,

Barbiroli,

Fuschini

2024

IEEE Open J. Antennas Propag.

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Wireless channel properties in industrial environments can differ from residential or office settings due to the considerable impact of heavy machinery that triggers intricate multipath propagation effects and strong blockage effects. Previous investigations on wireless propagation in factories often consisted of empirical models, that is simple analytical formulas based on measurement data. Unfortunately, they usually lack in flexibility, since they seldom include geometrical parameters describing the industrial scenario and therefore turn out reliable only in industrial scenarios sharing the same propagation characteristics as those where the measurements were performed. In response to this limitation, this article harnesses the power of Machine Learning to model propagation markers like path loss, shadowing, and delay spread in the industrial environment. By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. Results are in fair overall agreement with previous studies and highlight some trends about the sensitivity of the propagation parameters to the considered input features.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment

Zadeh,

Barbiroli,

Fuschini

2024

IEEE Open J. Antennas Propag.

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“…In [89], data from online sources such as OpenStreetMap and various Geographical Information Systems were collected to construct a machine learning model aimed at predicting cellular coverage in metropolitan regions. This model demonstrated the capability to promptly estimate path loss, even in the absence of training data from the physical measurement campaign.…”

Section: Reviewed Papers On Machine-learning-based Path Loss Modelsmentioning

confidence: 99%

“…In an attempt to present a reliable and stable ML model in [31,[75][76][77][82][83][84][85][87][88][89]91,92] for high-band channels in urban environments, the k-NN, MPL, CNN, and SVR models performed similarly across all frequencies under consideration, with the random forest model (RF) delivering the most accurate predictions in [77,81] and the XGBoost model in [83] in an urban environment.…”

Section: Research Gapsmentioning

confidence: 99%

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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5G Base Station Electromagnetic Field Strength Estimation Method in Complex Hotspot Area using Deep Learning

Park,

Ryu,

Lee

et al. 2024

2024 IEEE International Symposium on Electromagnetic Compatibility, Signal &Amp;amp; Power Integrity (EMC+SIPI)

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Path Loss Prediction in Urban Areas: A Machine Learning Approach

Cited by 5 publications

References 5 publications

A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment

A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment

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

5G Base Station Electromagnetic Field Strength Estimation Method in Complex Hotspot Area using Deep Learning

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