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Preliminary perspectives on 3GPP standardization of the propagation channel model for FR3 bands for NR

Science China(Information Sciences) 2025年第3期68卷 391-392页

作者： Pan TANG Jianhua ZHANG Huixin XU Haiyang MIAO Ximan LIU State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications

Introduction of the study item on the channel model for FR3 bands for NR. According to the 3rd generation partnership project(3GPP) schedule, 3GPP work for the sixth generation(6G) typically starts in 2024 during Release 19. This release aims to establish the technical foundations for 6G and include preliminary work on new 6G capabilities.

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IncEFL:a sharing incentive mechanism for edge-assisted federated learning in industrial IoT

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Digital Communications and networks 2025年第1期11卷 106-115页

作者： Jiewei Chen Shaoyong Guo Tao Shen Yan Feng Jian Gao Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationsBeijing 100087China Kunming University of Science and Technology Kunming 650031China Yunnan Provincial Academy of Science and Technology Kunming 650228China

As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)***,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy *** address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data *** order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is *** addition,a weight dispersion evaluation scheme based on Wasserstein distance is *** study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training *** incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is ***,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.

关键词： Federated learning Data sharing Edge intelligence Incentives Contract theory

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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RecStream: Graph-aware Stream Management for Concurrent Recommendation Model Online Serving 31

RecStream: Graph-aware Stream Management for Concurrent Reco...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Shuxi Qi, Qi Sun, Haifeng Liao, Jianxin Wang, Jingyu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798891761971

Recommendation Models (RMs) are crucial for predicting user preferences and enhancing personalized experiences on large-scale platforms. As the application of recommendation models grows, optimizing their online serving performance has become a significant challenge. However, current serving systems perform poorly under highly concurrent scenarios. To address this, we introduce RecStream, a system designed to optimize stream configurations based on model characteristics for handling high concurrency requests. We employ a hybrid Graph Neural network architecture to determine the best configurations for various RMs. Experimental results demonstrate that RecStream achieves significant performance improvements, reducing latency by up to 74%. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Satellite-assisted 6G wide-area edge intelligence:dynamics-aware task offloading and resource allocation for remote IoT services

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Science China(Information Sciences) 2025年第2期68卷 248-268页

作者： Di ZHAO Rui DING Bin SONG State Key Laboratory of Integrated Services Networks Xidian University Hangzhou Institute of Technology Xidian University China Satellite Network Group Co. Ltd.

Future 6G networks will integrate non-terrestrial communication with mobile edge computing to enable wide-area edge intelligence, providing ubiquitous communication and computation services for everyone and everything. In this paper, we introduce the intelligent cloud-edge-device architecture into low Earth orbit(LEO) satellite-supported remote Internet of Things(IoT) networks to facilitate latency-sensitive and compute-intensive IoT applications. However, the dynamic spatio-temporal characteristics inherent in LEO satellite networks, such as the high volatility of task traffic and the high mobility of space nodes, pose severe challenges. For efficient task execution, we investigate the joint optimization of cross-region task offloading and cross-domain resource allocation, and innovatively propose a spatio-temporal attention-based proximal policy optimization(STA-PPO) algorithm. Specifically, the temporal attention-based actor network analyzes the timing dependencies of task arrival and makes time-variant decisions. Simultaneously, the spatial attention-based critic network captures the spatial variations of satellite motion and evaluates the topology-related value. We verify the effectiveness of the cloud-edge-device collaboration through extensive simulations. Numerical results demonstrate that the STA-PPO algorithm outperforms benchmarks, showing the lowest system delay and the highest link throughput.

关键词： 6G wide-area edge intelligence cross-region task offloading cross-domain resource allocation spatio-temporal attention proximal policy optimization

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Diffusion Models Meet network Management: Improving Traffic Matrix Analysis with Diffusion-Based Approach

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IEEE Transactions on network and Service Management 2025年第2期22卷 1259-1275页

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing Hefei University of Technology School of Computer Science and Information Engineering China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Macau University of Science and Technology Faculty of Information Technology China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. © 2004-2012 IEEE.

关键词： Level measurement

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Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection with Decentralized DRL in 6G-Enabled Space-Air-Ground networks

Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Ru Lin, Rongheng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

As sixth-generation (6G) networks extend to remote regions, efficient data collection from Internet of Things (IoT) devices faces challenges such as limited infrastructure, diverse latency requirements, and energy constraints. To address these, we propose a dual-mode data transmission strategy: (1) a UAV-satellite network for real-time delay-sensitive data, where wireless power transfer (WPT) is introduced to alleviate energy depletion caused by frequent data transmission, and a deadline τmax is set in each timeslot to maintain data freshness. (2) a carry-store mode for delay-tolerant data. Both aim to minimize energy consumption while maximizing data collection. Specifically, we develop a time scheduling policy to allocate data and energy transmission time for delay-sensitive data within τmax, and propose a novel centralized control, distributed execution framework using decentralized deep reinforcement learning (DRL), termed DRL-Schedule, designed to optimize UAV navigation and ensure energy-efficient, timely data collection. Simulation results demonstrate that DRL-Schedule achieves an average improvement of approximately 11.2% and 12.8% over the best baseline PPO in energy efficiency, when varying different number of UAVs and IoTs, respectively, and notably surpassing GA, Greedy and Random. © 2025 IEEE.

关键词： 6G Deep reinforcement learning Energy-efficiency UAV-LEO integrated data collection WPT

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Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid network

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Computers, Materials & Continua 2025年第5期83卷 2429-2447页

作者： Qinghai Ou Min Yang Jingcai Kong Yang Yang Beijing FibrLink Communications Co.Ltd. Beijing100071China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid *** such cases,a space-air-ground integrated network serves as an effective emergency *** study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity *** to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop *** the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory *** first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)*** iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop *** results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.

关键词： Energy efficiency effective capacity delay requirement power allocation smart grid space-air-ground integrated network

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Atlas: Towards Real-Time Verification in Large-Scale networks via a Native Distributed Architecture 25

Atlas: Towards Real-Time Verification in Large-Scale Network...

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20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Ma, Mingxiao Zhang, Yuehan Wang, Jingyu He, Bo Zhao, Chenyang Qi, Qi Zhuang, Zirui Sun, Haifeng Guo, Lingqi Guo, Yuebin Zhang, Gong Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Pengcheng Laboratory China *** China Huawei Technologies China

ISBN: (纸本)9798400711961

Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations;(2) scalable so as to accommodate today’s large-scale network architecture. Current DPV tools struggle to meet these requirements due to their centralized architecture. To be concrete, there is a bottleneck for a single-point server to perform real-time DPV tasks. Furthermore, a single-point server makes it hard to collect real-time data plane updates from every device in large-scale networks. This paper proposes Atlas, a native distributed data plane verification (DPV) solution, which systematically solves its scalability problem and improves its real-time performance. To achieve this, (1) Atlas designs a hierarchical distributed architecture for DPV in large-scale networks. (2) Atlas proposes a native approach to maximize the parallelism of the architecture. Both dataset-driven simulation experiments and deployments in the wild demonstrate that Atlas is capable of fast and scalable distributed verification. The advantages offered by Atlas continue to expand as network sizes increase. Compared to state-of-the-art solutions, Atlas is 2-7× faster while keeping each component lightweight. © 2025 Copyright held by the owner/author(s).

关键词： Packet loss

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16QAM-OFDM VLC System Based on Frequency Domain Precompensation and DNN Post-Equalization

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IEEE Internet of Things Journal 2025年第9期12卷 12278-12286页

作者： Zhang, Hongyang Yang, Aiying Xu, Hang Ji, Shaoxi Feng, Lihui Zhang, Zhenrong Zhang, Minglun Beijing Institute of Technology Key Laboratory of Photonics Information Technology School of Optics and Photonics Beijing100081 China Guangxi University Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Nanning530004 China Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China

Visible light communication (VLC) technology utilizes LEDs as light sources, offering several benefits, such as the use of license-free spectrum, green, and safe. However, conventional LED devices face challenges due to their restricted bandwidth and nonlinearity under different current levels. These issues lead to signal distortion and intersymbol interference (ISI), which are particularly problematic in high-order modulation schemes. In this work, we propose a low-complexity scheme combining S21 precompensation and shallow hidden layer deep neural networks (DNNs)-based post-equalization in a VLC system. At the transmitter, S21 precompensation is used to suppress the influence of high-frequency power fading. The post-equalization at the receiver side uses the least square (LS)-DNN cascaded equalization algorithm, where LS performs linear equalization and DNN performs nonlinear equalization. Since the S21 precompensation and LS algorithm have completed partial equalization, it can provide a good initial state for DNN training, thus speeding up the convergence of DNN. To better capture the dependency between symbols, we preprocess the dislocation filling before the data is sent to the input layer of DNN. In the experiment, a VLC system with the 3-dB bandwidth of 9 MHz is used. With S21 precompensation, its 3-dB bandwidth is expanded to about 43 MHz. The two hidden layer DNN is used to carry nonlinear equalization. If an 80-Mb/s 16QAM OFDM signal is transmitted in a 3-m VLC system, the scheme proposed can expand the available peak-to-peak voltage Vpp range by 30% compared with precompensation LS algorithm and achieve transmission over 9 m under 7% hard-decision forward error correction (HD-FEC) BER threshold of 3.8e-3. The comparison with LSTM-DNN-based scheme shows that the proposed scheme can improve the performance of VLC system in terms of BER and EVM, while the training complexity is 2-order of magnitude lower. © 2014 IEEE.

关键词： Forward error correction

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