Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recently, studies have shown that anomalies on graphs induce spectral shifts. Some supervised methods have improved the utilization of such spectral domain information. However, they remain limited by the scarcity of labeled data due to the nature of anomalies. On the other hand, existing unsupervised learning approaches predominantly rely on spatial information or only employ low-pass filters, thereby losing the capacity for multi-band analysis. In this paper, we propose Graph Autoencoder with Spectral Encoder and Spectral Decoder (GRASPED) for node anomaly detection. Our unsupervised learning model features an encoder based on Graph Wavelet Convolution, along with structural and attribute decoders. The Graph Wavelet Convolution-based encoder, combined with a Wiener Graph Deconvolution-based decoder, exhibits bandpass filter characteristics that capture global and local graph information at multiple scales. This design allows for a learning-based reconstruction of node attributes, effectively capturing anomaly information. Extensive experiments on several real-world graph anomaly detection datasets demonstrate that GRASPED outperforms current state-of-the-art models.

Spectrum resource allocation and flow control are important considerations in dual-connectivity (DC) technology. Currently, limited research exists on spectrum resource allocation and flow control when user equipment (UE) employs DC mode within the cloud radio access network (C-RAN) architecture. Therefore, we propose a spectrum resource allocation scheme for DC mode in C-RAN architecture. The scheme formulates the problem as an optimization to maximize a utility function, allocating spectrum resources based on an operator’s revenue, UE’s service priority, signal-to-interference-plus-noise ratio (SINR) at the UE’s channel, and system throughput. We also proposed a flow control method for various application scenarios in 6G wireless networks. The method selects transmission path for downlink data based on delay estimation and the channel’s SINR. Simulation results demonstrate the proposed scheme’s effectiveness in improving frequency resource utilization, operator’s revenue, and system throughput compared to existing schemes, while significantly reducing bit error rate for enhanced ultra-reliable low latency communication with minimal transmission delay increase.