arXiv:2511.05522v4 Announce Type: replace-cross Abstract: Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building h
The increasing complexity and demand for real-time optimization in wireless networks, coupled with advancements in deep learning, make this a timely innovation for addressing current computational bottlenecks.
Accurate, low-latency wireless channel modeling is critical for the development and optimization of 5G/6G networks, autonomous systems, and digital twin applications, directly impacting future connectivity and automation capabilities.
Traditional computationally intensive ray tracing methods for radio map generation are challenged by a potentially faster, AI-driven approach, enabling more dynamic and efficient wireless network management.
- · Telecommunications companies
- · AI/ML developers
- · Wireless network operators
- · Smart city developers
- · Traditional radio frequency (RF) simulation software providers
- · Companies reliant on static network planning models
Faster and more accurate wireless network planning and optimization becomes possible.
This could accelerate the deployment and performance of 5G and future wireless technologies, enabling more robust digital twin environments.
Improved wireless connectivity infrastructure could further propel the development of AI-driven autonomous systems and enhanced real-time data processing across multiple sectors.
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Read at arXiv cs.AI