PINN and GNN-based RF Map Construction for Wireless Communication Systems

Radio frequency (RF) map is a promising technique for capturing the characteristics of multipath signal propagation, offering critical support for channel modeling, coverage analysis, and beamforming in wireless communication networks. This paper proposes a novel RF map construction method based on a combination of physics-informed neural network (PINN) and graph neural network (GNN). The PINN incorporates physical constraints derived from electromagnetic propagation laws to guide the learning process, while the GNN models spatial correlations among receiver locations. By parameterizing multipath signals into received power, delay, and angle of arrival (AoA), and integrating both physical priors and spatial dependencies, the proposed method achieves accurate prediction of multipath parameters. Experimental results demonstrate that the method enables high-precision RF map construction under sparse sampling conditions and delivers robust performance in both indoor and complex outdoor environments, outperforming baseline methods in terms of generalization and accuracy.

Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction

As modern power systems continue to evolve, accurate power load forecasting remains a critical issue. The phase space reconstruction method can effectively retain the chaotic characteristics of power load from a system dynamics perspective and thus is a promising knowledge-based preprocessing method for power load forecasting. However, limited by its fundamental theory, there is still a gap in implementing a multi-step forecasting scheme in current studies. To bridge this gap, this study proposes a novel multi-step forecasting approach by integrating the PSR with neural networks. Firstly, the useful features in the phase trajectory obtained from the preprocessing of PSR are discussed in detail. Through mathematical derivation, the equivalent characterization of the PSR and another time series preprocessing method, patch segmentation, is demonstrated for the first time. Based on this prior knowledge, an image-based modeling perspective with the global and local feature extraction strategy is introduced. Subsequently, a novel deep learning model, namely PSR-GALIEN, is designed for end-to-end processing, in which the Transformer Encoder and 2D-convolutional neural networks are employed for the extraction of the global and local patterns in the image, and a multi-layer perception based predictor is used for the efficient correlation modeling. Then, extensive experiments are conducted on five real-world benchmark datasets to verify the effectiveness as well as to have an insight into the detailed properties. The results show that, comparing it with six state-of-the-art deep learning models, the forecasting performance of PSR-GALIEN consistently surpasses these baselines, which achieves superior accuracy in both intra-day and day-ahead forecasting scenarios. At the same time, a visualization-based method is proposed to explain the attributions of the forecasting results.