Path Loss Modeling in Wireless Communication Systems

Path loss modeling underpins the design, optimisation and deployment of wireless networks by quantifying the reduction in signal strength as electromagnetic waves travel through different environments. Traditional approaches fall into two main categories: empirical models, which derive statistical fits from extensive field measurements, and deterministic models, which simulate wave propagation via techniques such as ray-tracing. Empirical formulations—exemplified by the Close-In, Floating Intercept and Alpha-Beta-Gamma models—are valued for their simplicity and ease of parameterisation but may lack generality across disparate terrains and frequency bands. Deterministic methods, though capable of high fidelity in complex urban or indoor settings, often incur prohibitive computational cost and require detailed three-dimensional representations. The advent of millimetre-wave communications in 5G and future networks has heightened sensitivity to atmospheric absorption, blockage and diffraction, driving the exploration of hybrid and data-driven solutions. Machine-learning frameworks now integrate regression ensembles, neural networks and fuzzy systems to capture non-linear interactions between frequency, distance, clutter and antenna geometry. Such advances promise more accurate large-scale fading and shadowing characterisations, supporting dynamic spectrum access and fine-grained cell planning in Internet of Things, vehicular and remote-area applications.

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Path Loss Modeling in Wireless Communication Systems publication trend

The graph below shows the total number of articles in path loss modeling in wireless communication systems across all publications each year (not limited to Nature Index journals).

Technical terms

Path Loss: Reduction in signal power density as an electromagnetic wave propagates through space and obstacles.

Millimetre-Wave (mmWave): Frequency band between roughly 30 and 300 GHz, offering high bandwidth but susceptible to blockage and atmospheric attenuation.

Empirical Model: Statistical path loss formulation derived from measured data, typically characterised by distance-dependent exponent and shadowing term.

Ray-Tracing: Deterministic simulation technique modelling the trajectories of rays through reflections, diffractions and scattering in complex environments.

Stacking Ensemble-Regression: Machine-learning approach that combines multiple regression models via a higher-level meta-learner to improve predictive accuracy.

Gravitational Search Algorithm (GSA): Optimisation metaheuristic inspired by the law of gravity, used to fine-tune model parameters for enhanced performance.

Convolutional Neural Network (CNN): Deep-learning architecture specialised for extracting spatial features from grid-structured data such as images, applied here to infer path loss from satellite views.

References

1. Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning. Results in Engineering (2024).
2. Agile gravitational search algorithm for cyber-physical path-loss modelling in 5G connected autonomous vehicular network. Vehicular Communications (2024).
3. Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning. IEEE Access (2020).