Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Artificial Intelligence in Radio Channel Modelling: Progress and Challenges
share announcement

Artificial Intelligence in Radio Channel Modelling: Progress and Challenges

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 623

Special Issue Editors

Prof. Dr. Lorenzo Rubio

E-Mail Website
Guest Editor
iTEAM Research Institute, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: wireless communications; channel modelling; channel measurements
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vicent Miquel Rodrigo Peñarrocha

E-Mail Website
Guest Editor
iTEAM Research Institute, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: wireless communications; channel modelling; channel measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The definition and standardization of future IMT-2030 systems (6G and beyond) requires multi-dimensional reference models capable of accurately describing wave propagation characteristics, interference patterns, and the dynamic nature of radio channels. Modelling radio channels at millimetre-wave and terahertz frequency bands poses significant challenges due to the special propagation characteristics at these frequencies. This has led researchers to explore innovative methodologies that address the limitations of traditional models based on stochastic and deterministic approaches.

Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a transformative approach to radio channel modelling. By leveraging its ability to learn patterns from large data sets, AI provides a powerful tool to analyze and model complex, non-linear phenomena that are difficult to capture using traditional methods. In addition to improving model accuracy, AI also enables real-time adaptability, a critical capability in highly dynamic communication environments.

This Special Issue aims to provide a comprehensive view of how AI can significantly improve our ability to understand, predict, and optimize radio channels, addressing the challenges and taking advantage of the opportunities offered by this exciting technology. Key topics of interest for this Special Issue include, but are not limited to, the following:

  • Processing large channel measurement records;
  • Identification of propagation patterns;
  • Estimation of channel parameters;
  • Interpretability of AI-based models;
  • Channel estimation and prediction;
  • Inherent challenges of AI in channel modelling;
  • Channel modelling with application to advanced techniques based on extreme-MIMO and beamforming.

Prof. Dr. Lorenzo Rubio
Prof. Dr. Vicent Miquel Rodrigo Peñarrocha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • radio wave propagation
  • wireless channels
  • channel modelling
  • channel measurements

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

38 pages, 3954 KB  
Article
Geospatial Feature-Based Path Loss Prediction at 1800 MHz in Covenant University Campus with Tree Ensembles, Kernel-Based Methods, and a Shallow Neural Network
by Marta Moreno-Cuevas, José Lorente-López, José-Víctor Rodríguez, Ignacio Rodríguez-Rodríguez and Concepción Sanchis-Borrás
Electronics 2025, 14(20), 4112; https://doi.org/10.3390/electronics14204112 - 20 Oct 2025
Viewed by 328
Abstract
This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and [...] Read more.
This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and clutter height—and train Random Forests (RF), Gradient Boosting (GB), Support Vector Regression (SVR), Gaussian Processes (GP), and a shallow neural network (NN). A unified pipeline with 5-fold cross-validation (CV), seeded reproducibility, and Optuna-driven hyperparameter search is adopted; performance is reported as RMSE/MAE/R2 (mean ± sd). To contextualize feature reliability, we include Pearson correlation heatmaps and Variance Inflation Factors (VIFs), a systematic ablation of predictors, and TreeSHAP beeswarm analyses on held-out splits. We also evaluate spatially aware validation (blocked CV within route and leave-one-route-out checks) to mitigate optimism due to spatial autocorrelation. Results show that multivariate ML consistently outperforms classical empirical formulas (COST-231, ECC-33) in this campus setting, with RF achieving the lowest errors across routes (RMSE ≈ 2.14/2.16/2.95 dB for X/Y/Z, respectively), while GB ranks second and kernel methods (SVR/GP) and the NN trail closely behind. Ablation confirms that distance plus coordinates drive the largest gains, with terrain/clutter providing route-dependent refinements. SHAP analyses align with these findings, highlighting stable, interpretable contributions of geospatial covariates. Spatial CV increases absolute errors moderately but preserves model ranking, supporting the robustness of conclusions. Overall, scenario-aware, multivariate ML yields material accuracy gains for smart-campus planning at 1.8 GHz. Full article
(This article belongs to the Special Issue Artificial Intelligence in Radio Channel Modelling: Progress and Challenges)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop