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Channel Characterization and Modeling for Future Wireless Communication Systems
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Channel Characterization and Modeling for Future Wireless Communication Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 723

Special Issue Editors

Dr. Pan Tang

E-Mail Website
Guest Editor
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: channel sounding; channel characterization; modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Yang

E-Mail Website
Guest Editor
Department of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China
Interests: radio propagation overseas; land-based maritime communication; massive MIMO

Special Issue Information

Dear Colleagues,

Channel characterization and modeling are essential for developing efficient, reliable wireless systems, as they directly influence performance metrics like data rates, latency, and coverage. With 6G technology advancing toward satellite-terrestrial integration, ultra-massive MIMO, integrated sensing and communication, AI-communication fusion, and Wi-Fi standards (e.g., Wi-Fi 6/7) are evolving to support higher throughput and lower latency. However, new challenges are emerging in channel research. These include complex propagation at higher frequencies, dynamic environmental effects, and the need for scalable, intelligent modeling techniques. This Special Issue invites contributions on fundamental theories and key technologies, including channel measurement, standardization, and modeling for 6G and Wi-Fi systems. Potential topics include, but are not limited to, the following:

  1. Channel characterization and modeling for satellite-terrestrial integrated networks.
  2. Air–ground channel measurement and modeling.
  3. Channel measurement and modeling for new technologies, e.g., XL-MIMO, ISAC, and RIS.
  4. Channel measurement and modeling in complex scenarios, e.g., IIoT, V2X, and marine communications.
  5. Channel measurement and modeling for new bands of 6G and Wi-Fi.
  6. Machine learning-based channel modeling in dynamic environments.
  7. Key technologies of channel digital twin.
  8. Standardization of channel models for future wireless systems.

Dr. Pan Tang
Dr. Kun Yang
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 2600 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

  • channel measurement
  • channel characterization
  • channel modeling
  • 6G
  • Wi-Fi

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Published Papers (1 paper)

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Research

17 pages, 876 KB  
Article
Transformer-Enhanced Localization via Adaptive PDP Representation Under Dynamic Bandwidths
by Lei Cao, Tianqi Xiang, Weiyan Chen, Yicheng Wang, Yuehong Gao and Xin Zhang
Sensors 2026, 26(5), 1486; https://doi.org/10.3390/s26051486 - 27 Feb 2026
Viewed by 384
Abstract
Accurate wireless positioning has remained challenging under dynamic bandwidth conditions and outdoor multipath environments that are typical in Internet of Things (IoT) and autonomous aerial vehicle (AAV) applications. Conventional learning-based localization methods rely on bandwidth-specific channel state information (CSI) representations, which causes the [...] Read more.
Accurate wireless positioning has remained challenging under dynamic bandwidth conditions and outdoor multipath environments that are typical in Internet of Things (IoT) and autonomous aerial vehicle (AAV) applications. Conventional learning-based localization methods rely on bandwidth-specific channel state information (CSI) representations, which causes the trained models to be inapplicable or less adaptive when the signal bandwidth differs from that used during training. To overcome this limitation, a unified and neural network-oriented framework is proposed, which constructs bandwidth-adaptive power delay profile (PDP) representations for learning-based models. A PDP preprocessing scheme through adaptive zero-padding and oversampled IFFT of heterogeneous CSI is introduced to generate dimension-consistent and delay-aligned neural network inputs. To enhance robustness, a sub-band-sliced PDP representation is developed to enhance model robustness, where each bandwidth is divided into equal-width sub-bands whose PDPs are independently processed and organized as Transformer tokens. A dedicated Transformer is designed to get the location estimation from PDPs of multi-access points. Simulation results have demonstrated that the proposed preprocessing-PDP-plus-Transformer framework achieves superior cross-bandwidth generalization and localization accuracy, compared to analytical and learning-based baselines. Full article
(This article belongs to the Special Issue Channel Characterization and Modeling for Future Wireless Communication Systems)
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