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In the context of modern wireless communications, the ability to accurately model the multipath propagation environment is essential for optimizing coverage, resource allocation, and network performance. Radio frequency (RF) maps offer a compact representation of these phenomena, but their construction faces challenges such as scarce measurements and the need to generalize across diverse scenarios. A novel approach combines physics-informed neural networks (PINNs) with graph neural networks (GNNs) to address both cross-scene generation and within-scene completion. While the PINN incorporates electromagnetic propagation constraints to obtain a physically coherent relationship between the receiver position and multipath parameters — gain, time of arrival, and angles — the GNN models spatial correlations between neighboring receivers, ensuring the geometric consistency of the map. This approach outperforms techniques based on images, diffusion, or interpolation, especially when observations are scarce, and enables faithful reconstruction of the channel impulse response using metrics such as peak-weighted dynamic time warping.
The practical application of these models extends beyond the laboratory. Telecommunications operators, smart infrastructure companies, and IoT service providers can benefit from AI for business solutions that automate network deployment and coverage planning. For example, integrating AI agents trained with these maps enables real-time decisions on handovers or power adjustments. Furthermore, the ability to complete maps from few samples drastically reduces the need for costly measurement campaigns. At Q2BSTUDIO, we develop custom applications that incorporate this type of artificial intelligence, adapting it to each client's specific environments, whether for 5G networks, smart cities, or indoor positioning systems.
The synergy between PINN and GNN not only improves accuracy but also opens the door to new functionalities. For instance, the model can be trained with data from one area and then applied to another with similar characteristics — known as cross-scene generation — a key aspect for custom software that needs to scale geographically. Likewise, the platform can be deployed on AWS and Azure cloud services, facilitating distributed processing and continuous map updates. From a business perspective, these maps feed Power BI dashboards and other business intelligence service tools, allowing operators to visualize coverage and detect anomalies. Cybersecurity is also enhanced, as precise channel knowledge helps identify potential interference points or signal spoofing attacks.
In short, the combination of physics and machine learning is transforming how we understand and manage the radio spectrum. Q2BSTUDIO offers consulting and development to implement these techniques in real projects, integrating AI agents and process automation that maximize network performance. With a focus on data quality and adaptability, our solutions enable organizations to make the most of the information contained in multipath maps, reducing operational costs and improving the end-user experience.
