Special Topic on Digital Twin Online Channel Modeling for 6G and Beyond

Channel characterization and modeling are fundamental to communication system design, development, testing, and deployment. As the innate digital twin of wireless channels, channel models replicate real-world channel behaviors, e.g., large-scale/small-scale fading, spatio-temporal-frequency non-stationarity, through mathematical and data-driven methods. This enables simulation-based validation across system development stages—from protocol design to network optimization—without costly physical testing.

In 6G/B6G, new frequency bands (e.g., centimeter wave and millimeter wave) and new scenarios (e.g., integrated sensing and communication (ISAC), unmanned aerial vehicle (UAV) communications) have introduced highly dynamic, complex channel characteristics. The critical task is to conduct channel measurements and modeling for diverse bands/scenarios, challenged by technological advancements: Larger antenna arrays and higher resolution have driven transitions from traditional static to dynamic measurements, generating massive datasets. In this case, AI has become an essential method to process big data, improve model accuracy, and enable real-time channel adaptation, overcoming bottlenecks in high-frequency and dynamic scenario analysis.

In this special issue, a series of articles are presented to address the challenges in channel measurement and modeling for next-generation wireless networks, offering innovative solutions to advancing the field. These articles cover a diverse range of topics, including novel measurement methodologies for complex scenarios, machine learning-enhanced channel data processing technologies, digital twin-enabled modeling frameworks, and applications in emerging 6G use cases such as ISAC and UAV communications. The call for papers of this special issue has attracted a strong response of high-quality submissions, demonstrating broad academic and industrial interest in overcoming the technical bottlenecks of channel characterization across frequency bands and scenarios. After two rounds of rigorous peer review, six excellent papers have been selected for publication in this special issue, which are presented as follows.

The first paper, titled “Channel Measurement and Analysis of Radar Cross Section of the Human Body for ISAC at 26 GHz Frequency Band”, proposes a systematic approach to characterizing electromagnetic scattering from human bodies in ISAC systems, leveraging multi-angle measurements and ray-tracing analysis to optimize joint communication-sensing performance in urban micro-cellular environments.

The second paper, titled “Research on Space Network Emulation System Based on User-space Network Stack NOS”, presents a novel user-space network stack (NOS)-based framework to realistically emulate satellite and aerial network channels, enabling validation of space-air-ground integrated communication systems under dynamic propagation conditions and reducing development complexity through technologies like Open vSwitch (OVS) and traffic control (TC).

The third paper, titled “A Machine Learning-based Channel Data Enhancement Platform for Digital Twin Channels”, introduces a generative adversarial network (GAN)-driven platform to address channel data scarcity, demonstrating how AI can generate statistically realistic channel samples from sparse measurements to accelerate digital twin channel development for 6G networks.

The fourth paper, titled “6G Digital Twin Enabled Channel Modeling for Beijing Central Business District”, proposes a scenario-specific digital twin framework that integrates LiDAR point clouds, RGB images, and crowdsourced data to characterize ultra-dense urban channels, providing insights for network deployment in high-rise commercial zones by mimicking channel non-stationarity and consistency.

The fifth paper, titled “Channel Knowledge Maps for 6G Wireless Networks: Construction, Applications, and Future Challenges”, establishes a knowledge graph-based architecture to systematically organize channel data, models, and engineering experiences, facilitating intelligent decision-making in multi-band and multi-scenario communication systems through the concept of channel knowledge maps (CKMs).

The sixth paper, titled “A Review of Air-to-Ground Channel Measurement and Modeling for Low-altitude UAVs”, synthesizes recent advancements in low-altitude UAV air-to-ground (A2G) channel research, providing a comprehensive overview of measurement campaigns, modeling approaches, and future directions critical to 6G aerial network design, with a focus on millimeter-wave scenarios beyond suburban environments.

To conclude, we hope this special issue serves as a valuable resource for researchers, practitioners, and students engaged in 6G/B6G channel measurements and modeling. It aims to inspire innovative solutions for dynamic channel challenges and drive advancements in AI-integrated channel modeling. We sincerely thank all authors, reviewers, and editorial staff for their contributions, which were crucial to curating this collection. We trust these articles will offer insightful guidance and foster new perspectives in wireless channel characterization for next-generation networks.