Localization, Navigation, and Channel Modeling: AI Solutions for Complex Indoor and Urban Environments

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 612

Special Issue Editors


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Guest Editor
Department of Telecommunication Systems, Technische Universität Berlin, Berlin, Germany
Interests: communications; machine learning; signal processing; radio maps; localization

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Guest Editor
Electrical Engineering and Computer Science Department, Technische Universität Berlin, 10587 Berlin, Germany
Interests: communications theory; information theory; channel and source coding; wireless communications
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Special Issue Information

Dear Colleagues,

This Special Issue explores the latest advancements in localization and navigation within complex environments, such as indoor and urban settings, as well as innovations in radio map prediction and the broader field of propagation modeling. These areas are critical for enabling accurate positioning, efficient wireless communication, and reliable predictions of electromagnetic wave behavior in environments where traditional distance estimate-based systems such as GNSS fail due to complex signal attenuation, multipath effects, or obstructions caused by buildings and other structures present in the propagation environment.

Localization in complex environments faces significant challenges due to structural complexity, dynamic conditions, and signal interference. Indoor positioning systems (IPSs) employ technologies such as Wi-Fi, Bluetooth, Ultra-Wideband (UWB), magnetic fields, and visual markers to achieve high levels of accuracy. However, these methods often struggle with environmental variability and computational demands. Multi-sensor fusion techniques that integrate data from inertial sensors, cameras, and spatial models have emerged as robust solutions to improve accuracy. Machine learning further enhances these systems by enabling real-time adaptability to environmental changes. In urban settings, hybrid localization methods combining GNSS with alternative technologies such as barometers or radio frequency-based approaches ensure seamless transitions between indoor and outdoor environments while maintaining precision.

Radio maps are essential tools for quantifying wireless signal distributions across geographical areas and are integral to applications like network planning, resource allocation, and fingerprinting-based localization. Their generation has been advanced through deterministic models and recent deep learning-based approaches. Deterministic methods, such as ray tracing, simulate radio wave propagation by accounting for environmental factors like geometry, material properties, and interactions such as reflection or scattering. While these methods are highly accurate, they are also computationally intensive. Deep learning-based models offer a faster (and potentially more precise) alternative by leveraging parallelization and GPU acceleration to predict radio wave behavior efficiently. These methods can incorporate sparse measurements alongside environmental information to address inaccuracies while maintaining high-speed predictions.

Recent advancements in channel modeling have introduced methods capable of providing highly accurate predictions of wireless channel characteristics while addressing computational efficiency. These approaches leverage detailed representations of the propagation environment to model complex phenomena such as multipath propagation, delay spreads, and angular spreads. By integrating physical principles with data-driven techniques, these models enable accurate predictions of metrics like Received Signal Strength (RSS) and Channel State Information (CSI), which are crucial for modern wireless systems.

This Special Issue invites contributions across its core themes:

- Localization and navigation in complex environments such as indoor and urban settings; 
- Radio map prediction using deep learning-based methods; 
- Learning propagation models for accurate predictions of electromagnetic wave behavior and observed characteristics such as RSS or CSI; 
- Novel approaches that combine physics-based modeling with machine learning to enhance channel modeling efficiency.

By fostering interdisciplinary research across these domains, this collection aims to advance the understanding of localization systems, radio map generation, and propagation modeling while addressing the growing demands of modern wireless communication networks and location-based services.

Dr. Çağkan Yapar
Dr. Giuseppe Caire
Guest Editors

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Keywords

  • deep learning-based propagation models
  • deep learning-based channel prediction
  • radio maps
  • indoor localization
  • urban localization
  • indoor positioning
  • urban positioning
  • indoor navigation
  • urban navigation
  • artificial intelligence
  • machine learning
  • deep learning
  • wireless communications

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Published Papers (1 paper)

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Review

27 pages, 3376 KiB  
Review
A Recent Survey on Radio Map Estimation Methods for Wireless Networks
by Bin Feng, Meng Zheng, Wei Liang and Lei Zhang
Electronics 2025, 14(8), 1564; https://doi.org/10.3390/electronics14081564 - 12 Apr 2025
Viewed by 388
Abstract
As a visualization of radio frequency environment state, radio maps are of significant importance in enhancing the efficiency of spectrum resources and the quality of service of general-purpose wireless networks. For a comprehensive review on radio map estimation (RME) methods, representative achievements in [...] Read more.
As a visualization of radio frequency environment state, radio maps are of significant importance in enhancing the efficiency of spectrum resources and the quality of service of general-purpose wireless networks. For a comprehensive review on radio map estimation (RME) methods, representative achievements in recent years on RME are categorized. Firstly, according to the extent of dependency on model knowledge, existing RME methods are categorized into three major classifications for the first time: model-driven methods, data-driven methods, and hybrid model data-driven methods. Subsequently, typical works in each classification of RME methods, as well as their pros and cons, are illustrated in detail. Moreover, this survey compiles public datasets for radio maps and points out that hybrid simulation measurement datasets are crucial for RME. Finally, future directions on the RME problem are discussed. Unlike existing surveys, this survey not only ensures academic accuracy in its literature review, but also preserves the evolutionary trajectory of RME methods, enabling readers to quickly grasp the history and development trends of RME. Full article
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