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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Qian, Xiqin Lan, Chuwen Gao, Zehua Beijing Laboratory of Advanced Information Networks Beijing Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400710094

This article proposes a rectangular cavity resonator that can achieve liquid refractive index sensing. Place a square solid dielectric block inside the cavity, drill uniformly distributed holes in front of and on top of the dielectric block, and observe the small changes in the refractive index of the liquid by measuring the changes in resonant frequency and microwave transmission coefficient S21. After analyzing various structural parameters, the article achieved a sensitivity of 4122MHz/RIU near 26.3GHz, which is 2.37 times higher than the sensitivity of solid structure sensors. This result also provides a new approach for the design and performance improvement of future high-sensitivity refractive index sensors. © 2024 Copyright held by the owner/author(s).

关键词： Cavity resonators

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Joint Data Offloading and Resource Allocation for Terrestrial-Satellite Internet of Things 9

Joint Data Offloading and Resource Allocation for Terrestria...

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Guan, Jingyi Yin, Fangfang Liu, Danpu Jin, Libiao Li, Shufeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798400708909

In this paper, we propose a terrestrial-satellite Internet of Things (IoT) to integrate the geostationary earth orbit (GEO) satellite and the terrestrial small-cell base stations (SBSs) to achieve efficient data offloading. A terrestrial-satellite networks (TSN) framework with multi-access edge computing (MEC) is investigated for two kinds of multimedia services, computation-sensitive service and high-data-rate service. We aim to minimize the sum latency of services while satisfying the distinct quality of service (QoS) requirements jointly determined by data offloading (DO) and computing resource allocation (CA). Since the optimized problem is nonconvex, we decompose the formulated problem into two subproblems: 1) the DO subproblem minimizing the latency of two heterogeneous services, which is solved by one-to-many matching and 2) the CA subproblem that is solved by leveraging the convex optimization. Furthermore, an alternating iteration method is adopted to obtain the near-optimal solution with low computation complexity. Finally, simulation results are conducted with different system parameter configurations to verify the effectiveness of our proposed joint scheme. © 2023 Copyright held by the owner/author(s)

关键词： Resource allocation

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Delay-bounded Topology Construction and Routing Integration for Time-critical Services 36

Delay-bounded Topology Construction and Routing Integration ...

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36th IEEE/IFIP network Operations and Management Symposium, NOMS 2023

作者： Huang, Xiaofu Shou, Guochu Liu, Yaqiong Gao, Zehua Hu, Yihong Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781665477161

Applications such as virtual/augmented reality, autonomous systems, and telemedicine require delay-bounded transmission. This paper combines the software-defined network (SDN) paradigm and proposes an integrated solution for topology construction and routing (TCR). First, the solution gives a delaybounded constraint for adding valid links, which can be adapted to various topology construction methods. Then, topology discovery, resource management, flow management, and route selection are performed in the orchestration and configuration plane. TCR enables time-sensitive network management and configuration with flexibility. The performance of the TCR is evaluated in a typical industrial network topology. The experimental results show that the TCR can effectively reduce the average path length (APL), guarantee bounded delay, and is beneficial for load balancing. © 2023 IEEE.

关键词： Topology

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Optimizing Community Detection in Complex networks by Regrouping Gateways 9

Optimizing Community Detection in Complex Networks by Regrou...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Luo, Hongli Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9798350317251

Community detection that can extract potential hidden structures and key information of large-scale complex networks has been studied so many years. However, there exists a possibility of grouping the gateway incorrectly which is the central node communicates with the outside, thus resulting in a large loss of modularity. Here we propose a regrouping gateways (RG) algorithm for community detection to optimize the existing community partitions which have been divided by various algorithms. We design a heuristic optimization algorithm based on a breadth-first search strategy with less complexity than Leiden to improve modularity. Experiments carried out on multiple real networks show that RG exhibits good generality in effectively increasing the modularity and reducing the randomness of community detection algorithms. © 2023 IEEE.

关键词： Optimization

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Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

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China Communications 2024年第1期21卷 88-101页

作者： Shanzhi Chen Xinxin He Rui Zhao Jinling Hu Xin Zhang China Information and Communication Technology Group Co. Ltd.(CICT)the State Key Laboratory of Wireless Mobile CommunicationsBeijing 100083China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China CICT Connected and Intelligent Technologies Co. LtdChina Academy of Telecommunication TechnologyBeijing 100191China CICT Mobile Communications Technology Co.Ltd.

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and *** millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary *** this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage *** vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

关键词： C-V2X enabling technologies mmWave NR-V2X

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Arrival Angle Estimation Based on Transfer Learning in Complex Environments 9

Arrival Angle Estimation Based on Transfer Learning in Compl...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Li, Xingjian Chu, Xinghe Wang, Luhan Lu, Zhaoming Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Infrmation Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9798350317251

Signal Angle-of-Arrival estimation has been widely studied as an important sensing technology in Integrated Sensing and Communications, but its performance is limited by complex multipath environments and the scale of antenna arrays. In recent years, learning-based signal angle-of-arrival estimation methods have made breakthroughs, but the effectiveness of learning methods is closely related to the richness of datasets. In the new generation of communication systems, the large-scale deployment of base stations for sensing needs, the data set is difficult to cover a large number of complex and changeable scenarios, and there are problems such as excessive collection scale and difficult labeling. In order to achieve effective estimation of signal angle-of-arrival in complex environments, this paper uses the method of transfer learning to reduce the cost of data set collection and improve the accuracy of estimation. The transfer learning in this paper uses a domain adversarial model to achieve unlabeled transfer from simple to complex scenes. The experiments set up multiple scenarios such as multi-source, multi-path, and array defects. The migration results show that the method can effectively improve the performance of angle-of-arrival estimation in most scenarios, and verify the adaptability of the proposed method to complex environments. © 2023 IEEE.

关键词： Learning systems

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Dynamic Trajectory Design for Multi-UAV Assisted Long-Term Data Collection Tasks

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Zijing Guo, Yijun Hao, Jianjun Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

In this paper, we focus on a long-term data collection task executed by multiple cooperative UAVs, and innovatively consider the scenario that UAVs may dynamically exit and rejoin the network to maintain continuous data collection. By considering both transmission rate and energy consumption, we maximize a multi-objective function through optimizing UAV trajectories, device access scheduling, and recharge scheduling. To address the challenges posed by fluctuating UAV numbers, we model the optimization problem as a decentralized partially observed Markov decision process (Dec-POMDP) and propose a Multi-Agent Dynamic QMIX (MADQMIX) algorithm that can adapt to changes in the number of agents. Additionally, to mitigate the dimensionality issue of output actions, we decompose joint actions into sub-agents for decision-making. Numerical results demonstrate the performance gains in both transmission rate and energy consumption achieved by the proposed algorithm, which increase with the scale of the UAV network. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Refine Reinforcement Learning for Safety Training of Autonomous Driving 27

Refine Reinforcement Learning for Safety Training of Autonom...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Yang, Fukun Hu, Zhiqun Zhang, Yuanming Huang, Hao Wang, Guixin Beijing University of Posts and Telecommunications Beijing Key Laboratory for Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798331505929

The random exploration nature of reinforcement learning (RL) impedes the way to achieve human-like autonomous driving, owing to the prohibitively high safety requirement. In this paper, we propose a deep refine reinforcement learning (DR2L) approach to removing non-safety-critical actions and reconstructing critical ones, which effectively enhance the efficiency of exploration. The core is to design an action filter based on two-stage vehicle motion model to calculate the critical value of dangerous actions and reconstructe the action space by filtering out the obvious incorrect actions. Besides, we propose to use the beta distribution as the stochastic policy, which eliminates the bias of the Gaussian policy and provides faster convergence. Finally, we design spacial-temporal attention network to extract hidden information of environment as the state to enhance the performance of RL. The simulation shows that DR2L can effectively improve the safety of agent during training process. Our resutls show that the beta policy provides significantly faster convergence over the Gaussian policy when both are used with proximal policy optimization (PPO). © 2024 IEEE.

关键词： Deep reinforcement learning

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Vulnerable Nodes Identification Algorithm Based on Virtual Loop Dependence in Power Communication networks 9

Vulnerable Nodes Identification Algorithm Based on Virtual L...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ye, Jingyi Wu, Muqing Zhao, Min Beijing University of Post and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Post and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9798350317251

With the deepening of coupling between power communication network (PCN) and power system, the failure of PCN will seriously affect the safety and stability of the power system. This paper finds that PCNs have the high clustering coefficients characteristic. Based on this, the paper proposes the vulnerable nodes identification algorithm of PCNs based on virtual loop dependence (VLD), which considers information from three perspectives: the global location, the inter-node relative relationship and the neighborhood topology, to obtain the node vulnerability score. From the global perspective, VLD considers the node core position in the network and proposes the relative position centrality to weak the rich club. From the local perspective, VLD constructs virtual edge dependence and determines the node criticality by combining the influence of loop structure on dependence reduction. Finally, VLD ranks nodes by the vulnerability score. The paper simulates and analyzes six PCNs. By comparing with seven different algorithms, it is proved that the VLD algorithm can distinguish the similar nodes in a finer manner. Through the network efficiency loss and the network robustness decrease after attacking the critical nodes, the VLD algorithm in this paper is proven to be more accurate and more effective. © 2023 IEEE.

关键词： Telecommunication networks

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Adversarial Samples Defense Strategy Based on Service Orchestration 8

Adversarial Samples Defense Strategy Based on Service Orches...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Mengxin Qiu, Xiaofeng Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

Deep neural networks (DNNs) are playing an increasingly significant role in the modern world. However, they are weak to adversarial samples that are generated by adding specially crafted perturbations. Most defenses against adversarial samples focused on refining the DNN models, which often sacrifice the performance and computational cost of models on benign samples. In this paper, we propose a manifold distance detection method to distinguish legitimate samples and adversarial samples by measuring the different distances on the manifold. The manifold distance detection method neither modifies the protected models nor requires knowledge of the process for generating adversarial samples. Inspired by the effectiveness of the manifold distance detection, we demonstrated a well-designed orchestrated defense strategy, named Manifold Distance Judge (MDJ), which selects the best image processing method that will effectively expand the manifold distance between legitimate and adversarial samples, and thus, enhances the performance of the following manifold distance detection method. Tests on the ImageNet dataset, the MDJ is effective against the most adversarial samples under white-box, gray-box, and black-box attack scenarios. We show empirically that the orchestration strategy MDJ is significantly better than Feature Squeezing on the recall rate. Meanwhile, MDJ achieves high detection rates against CW attack and DI-FGSM attack. © 2022 IEEE.

关键词： Neural network models

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