RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MB-RMR within a single area, eliminating the need for multi-region datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multi-band data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MB-RMR in environments with limited data availability.

Robust Security Analysis Based on Random Geometry Theory for Satellite-Terrestrial-Vehicle Network

Driven by B5G and 6G technologies, multi-network fusion is an indispensable tendency for future communications. In this paper, we focus on and analyze the \emph{security performance} (SP) of the \emph{satellite-terrestrial downlink transmission} (STDT). Here, the STDT is composed of a satellite network and a vehicular network with a legitimate mobile receiver and an mobile eavesdropper distributing. To theoretically analyze the SP of this system from the perspective of mobile terminals better, the random geometry theory is adopted, which assumes that both terrestrial vehicles are distributed stochastically in one beam of the satellite. Furthermore, based on this theory, the closed-form analytical expressions for two crucial and specific indicators in the STDT are derived, respectively, the secrecy outage probability and the ergodic secrecy capacity. Additionally, several related variables restricting the SP of the STDT are discussed, and specific schemes are presented to enhance the SP. Then, the asymptotic property is investigated in the high signal-to-noise ratio scenario, and accurate and asymptotic closed-form expressions are given. Finally, simulation results show that, under the precondition of guaranteeing the reliability of the STDT, the asymptotic solutions outperform the corresponding accurate results significantly in the effectiveness.