arXiv subjects

Dengao Li

Publications and source records attributed to Dengao Li.

3 recordsLinked to original sources

arXiv2026

Security-Aware Pinching-Antenna Systems (PASS): Physical-Layer Security Transmission

This paper investigates heterogeneous secure multi-user transmission in pinching-antenna systems (PASS), where dynamically adjustable pinching antennas reshape both guided-wave and free-space propagation to improve communication and confidentiality performance. Unlike conventional physical-layer security designs that represent different security requirements merely through weights or thresholds, heterogeneous services may change the logical role of each receiver for each information stream. To address this issue, we establish a unified role-dependent PASS transmission framework comprising low-, medium-, and high-security modes. These modes respectively maximize the minimum legitimate-user rate, protect confidential streams against external eavesdroppers, and further prevent non-target legitimate users from intercepting unauthorized information. The resulting joint optimization of information beamforming, artificial noise, and pinching-antenna positions is formulated as a long-horizon continuous-control problem. Two learning-based controllers are then developed to provide complementary complexity-performance tradeoffs. First, heterogeneous security-aware proximal policy optimization (HSPPO) directly transforms mode-specific rate and secrecy violations into normalized smooth feedback embedded in the proximal-policy-optimization advantage, enabling lightweight and violation-sensitive control. Second, multi-relational hierarchy-aware diffusion policy optimization (MRHA-DPO) combines a PASS-aware multi-relational graph encoder, a graph-conditioned hierarchical velocity network, and exact-inversion DPO training to achieve topology-aware and expressive control. The proposed framework enables a common PASS platform to flexibly support service-dependent confidentiality requirements while balancing online efficiency and control capability.

eess.SP↗

arXiv2025

PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming

A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the $\max$ operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

eess.SP↗

arXiv2022

Graph Neural Networks: Taxonomy, Advances and Trends

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.

cs.LG↗