Xing Guo

With the development of the low-altitude aerial intelligent network, the collaborative work between diverse terminal devices poses new challenges to distributed spectrum management. One key challenge lies in the difficulty of obtaining global or specific regional spectrum states, due to deployment costs and physical constraints, monitoring blind spots still exist in current spectrum sensing networks. Therefore, this letter proposes the spatial spectrum inference model (SSIM), which could achieve cross-regional and cross-band inference. This model innovatively integrates multi-domain information and design a topology optimization mechanism to achieve adaptive modeling of the frequency-spatial coupling relationship. Furthermore, aiming at the local correlation and long-range dependency of multi-band frequency, a Bidirectional Spectrum Convolutional Network (BiSCN) is designed to capture multi-granularity features. Experiments based on real data have shown that SSIM achieves better inference performance compared to the state-of-the-art models.

To provide more stable and precise communication services for users, accurately predicting user behavior is an essential task. However, this task faces many challenges in practice, especially how to effectively represent and model the dynamic relationships between user behaviors and how to analyze the interpretability of the model. To address these challenges, we propose a dynamic graph-based method to establish dynamic associations between user behaviors and construct a spatial-temporal dynamic graph dual-channel (STDGDC) model. The core idea of the STDGDC model is to establish a full-chain technical system encompassing “dynamic modeling, conditional fusion, and mechanism interpretation.” This method characterizes complex service behavior patterns through constructing dynamic spatio-temporal graphs, while incorporating environmental factors as regulatory variables during feature fusion. The conditional fusion mechanism not only preserves predictive accuracy through dynamic parameter calibration but also enhances model interpretability. Considering the huge user base and the sensitivity of personal privacy, we focus on cellular traffic data as a macro-level manifestation of user service behavior, and conducts experimental validation on public datasets. The experimental results validate the rationality and effectiveness of the proposed STDGDC model, thereby offering fresh research insights and practical approaches for predicting user behavior in the field of communication services.

Spectrum prediction is crucial in cognitive radio networks, and previous studies have introduced various spectrum modeling methods. However, these methods typically only capture static patterns from historical spectrum data, overlooking the dynamic nature of the spectrum environment. Therefore, this letter proposes a dynamic graph temporal-frequency dual-channel network (DGTFDN) to capture the time-varying correlations among multiple frequency bands and adaptively update and aggregate features. Additionally, the proposed method employs a dual-channel network to simultaneously model both non-structural and structural features from multiple frequency bands, and then utilizes an adaptive gating mechanism to fuse the two types of features. The experimental results show that the proposed method achieves better prediction results than other comparative methods, with improvements of 0.3638, 0.7828, and 2.7077% in MAE, MRSE, and MAPE respectively.