Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion
arXiv:2607.01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization. This paper proposes a unified RF map construction framework based on a physics-informed neural network (PINN) and a graph neural network (GNN), supporting both cross-scene generation and in-scene completion with 2D and 2.5D environmental representations. The PINN embeds electromagnetic propagation constraints to establish a physically consiste
This research addresses the increasing demand for efficient and accurate RF map generation essential for advanced wireless communication systems, driven by the proliferation of IoT and 5G/6G technologies.
Improved RF mapping techniques are crucial for optimizing wireless network performance, enabling more reliable communication, reducing energy consumption, and facilitating environment-aware AI applications.
The ability to generate and complete RF maps across diverse scenes using AI-driven methods significantly reduces the time and cost associated with traditional channel modeling, enabling faster network deployment and adaptation.
- · Telecommunication companies
- · Wireless equipment manufacturers
- · AI/ML companies specializing in radio systems
- · Defense sector
- · Traditional RF engineering service providers reliant on manual methods
- · Legacy channel modeling software vendors
More efficient and adaptable wireless networks become standard, improving connectivity and reducing operational costs.
The integration of AI into RF modeling will accelerate the development of self-optimizing wireless infrastructure, enhancing dynamic spectrum allocation and interference management.
This could lead to a proliferation of highly location-aware autonomous systems that utilize precisely modeled RF environments for navigation and task execution.
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Read at arXiv cs.AI