Pan Tang

Suhong Luo, Pan Tang, Jianhua Zhang et al.

The broadcast characteristics of sixth-generation (6G) low-earth orbit (LEO) satellite communications raise serious security issues. Movable antenna (MA) technology offers a promising physical layer security (PLS) solution by flexibly reconfiguring antenna positions to exploit additional spatial degrees of freedom. However, in highly dense LEO satellite constellations, the legitimate satellite and potential eavesdropping satellites may exhibit small angular separations, which poses significant challenges for the design of secure transmission schemes. To address this challenge, this paper proposes an MA-assisted secure transmission scheme for time-varying LEO satellite communications, where a ground station equipped with an MA array communicates with a serving satellite, while the other visible satellites are regarded as potential eavesdroppers. We maximize the average secrecy rate by jointly optimizing the transmit beamforming and MA positions. An alternating optimization (AO) framework is developed, where semidefinite relaxation is adopted for the beamforming optimization subproblem, while high-accuracy successive convex approximation (SCA) and low-complexity differential evolution (DE) algorithms are proposed for the MA position optimization subproblem. Numerical results demonstrate that the proposed MA-assisted LEO secure transmission scheme consistently achieves superior performance compared to the conventional fixed-position antenna scheme.

Pan Tang, Shixiang Tang, Huanqi Pu et al.

This paper presents MicroRCA-Agent, an innovative solution for microservice root cause analysis based on large language model agents, which constructs an intelligent fault root cause localization system with multimodal data fusion. The technical innovations are embodied in three key aspects: First, we combine the pre-trained Drain log parsing algorithm with multi-level data filtering mechanism to efficiently compress massive logs into high-quality fault features. Second, we employ a dual anomaly detection approach that integrates Isolation Forest unsupervised learning algorithms with status code validation to achieve comprehensive trace anomaly identification. Third, we design a statistical symmetry ratio filtering mechanism coupled with a two-stage LLM analysis strategy to enable full-stack phenomenon summarization across node-service-pod hierarchies. The multimodal root cause analysis module leverages carefully designed cross-modal prompts to deeply integrate multimodal anomaly information, fully exploiting the cross-modal understanding and logical reasoning capabilities of large language models to generate structured analysis results encompassing fault components, root cause descriptions, and reasoning trace. Comprehensive ablation studies validate the complementary value of each modal data and the effectiveness of the system architecture. The proposed solution demonstrates superior performance in complex microservice fault scenarios, achieving a final score of 50.71. The code has been released at: https://github.com/tangpan360/MicroRCA-Agent.

Haosen Wang, Chenglong Shi, Can Xu et al.

Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS, which leverages connection strength and multi-view self-expression to learn homophilous node representations. Specifically, we design a heterogeneous edge dropping augmentation strategy that enhances the homophily of augmented views. Moreover, we introduce a multi-view self-expressive learning method to infer the homophily between nodes. In practice, we develop two approaches to solve the self-expressive matrix. The solved self-expressive matrix serves as an additional augmented view to provide homophilous information and is used to identify false negatives in contrastive loss. Extensive experimental results demonstrate the superiority of HGMS across different downstream tasks.

Tianze Zhou, Fubiao Zhang, Pan Tang et al.

Recent advances have witnessed that value decomposed-based multi-agent reinforcement learning methods make an efficient performance in coordination tasks. Most current methods assume that agents can make communication to assist decisions, which is impractical in some situations. In this paper, we propose a semi-communication method to enable agents can exchange information without communication. Specifically, we introduce a group concept to help agents learning a belief which is a type of consensus. With this consensus, adjacent agents tend to accomplish similar sub-tasks to achieve cooperation. We design a novel agent structure named Belief in Graph Clustering(BGC), composed of an agent characteristic module, a belief module, and a fusion module. To represent each agent characteristic, we use an MLP-based characteristic module to generate agent unique features. Inspired by the neighborhood cognitive consistency, we propose a group-based module to divide adjacent agents into a small group and minimize in-group agents' beliefs to accomplish similar sub-tasks. Finally, we use a hyper-network to merge these features and produce agent actions. To overcome the agent consistent problem brought by GAT, a split loss is introduced to distinguish different agents. Results reveal that the proposed method achieves a significant improvement in the SMAC benchmark. Because of the group concept, our approach maintains excellent performance with an increase in the number of agents.