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arXiv 2025年

作者： Liu, Wenxuan Li, Zixuan Bai, Long Zuo, Yuxin Xu, Daozhu Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an China

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4% in event detection and 6.1% in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9% improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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A new CP-based non-overlapping FBMC-QAM system for future wireless communications

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China Communications 2025年第5期22卷 108-127页

作者： Ren Defeng Li Jing Sun Chuiqiang Zhang Guohua Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an 710121 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China China Academy of Space Technology (Xi'an) Xi'an 710000 China

A new cyclic prefix (CP)-based non-overlapping FBMC-QAM (CP-NO-FBMC-QAM) system with two prototype filters is proposed in this paper, which satisfies complex orthogonality conditions and good frequency energy confinement at the same time. We analyze its inter-carrier interference/inter-symbol interference (ICI/ISI) over multipath channels. Owing to the additional CP, the ISI of received symbols over multipath channels is eliminated in the proposed system, and the resulting improvement in the signal-to-interference ratio (SIR) performance is evaluated by theoretical analysis. Moreover, for the ICI caused by multipath propagation in received symbols, we develop a method that eliminates the ICI by frequency-domain channel estimation and equalization before the receiver filtering process. The proposed CP-NO-FBMC-QAM system and ICI cancellation method (ICICM) are validated by comparisons of implementation complexity, power spectral density (PSD), bit error rate (BER) and channel estimation performance with conventional CP-based orthogonal frequency division multiplexing (CP-OFDM) and FBMC-QaM systems.

关键词： Symbols Prototypes Filters Quadrature amplitude modulation Channel estimation Bit error rate Wireless communication Time-frequency analysis Interference Filter banks

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NestFL: Enhancing Federated Learning Through Nested Multi-Capacity Model Pruning in Heterogeneous Edge Computing

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IEEE Internet of Things Journal 2025年

作者： Zhou, Xiaomao Hu, Yujiao Jia, Qingmin Xie, Renchao Purple Mountain Laboratories Future Network Research Center Nanjing211111 China Beijing University of Posts and Telecommunications State Key Laboratory of networking and Switching Technology Beijing100876 China

Federated learning (FL) has emerged as a pivotal approach for edge-based distributed machine learning, yet it faces significant challenges due to the constrained capacities and heterogeneity of edge devices, including non-IID data distribution, communication constraints, and learning inefficiencies. Furthermore, a one-fits-all global model often fails to perform optimally across diverse participating devices. In this paper, we present NestFL, an efficient FL framework for edge computing that can jointly improve the training efficiency and achieve personalization. Specifically, NestFL innovates by incorporating distributed model pruning, creating a hierarchy of structured-sparse subnetworks tailored to the unique resource profiles of client devices. These subnetworks are integrated into a nested global model, ensuring parameter sharing without increasing the parameter space, thereby significantly reducing computational and communication burdens. Meanwhile, it implements a cross-training mechanism, allowing clients to train on a broader dataset and maintain consistent decision boundaries. Furthermore, a weighted aggregation mechanism is designed to improve training performance and maximally preserve personalization. Experimental results in different applications demonstrate the superiority of NestFL over the baseline approaches in terms of model accuracy, convergence speed, and personalization preservation. © 2014 IEEE.

关键词： Federated learning

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Adaptive Sampling and Joint Semantic-Channel Coding under Dynamic Channel Environment

arXiv

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arXiv 2025年

作者： Qi, Zhiyuan Feng, Yulong Qin, Zhijin Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China ZTE Corporation Nanshan District Shenzhen518055 China

Deep learning enabled semantic communications are attracting extensive attention. However, most works normally ignore the data acquisition process and suffer from robustness issues under dynamic channel environment. In this paper, we propose an adaptive joint sampling-semantic-channel coding (Adaptive-JSSCC) framework. Specifically, we propose a semantic-aware sampling and reconstruction method to optimize the number of samples dynamically for each region of the images. According to semantic significance, we optimize sampling matrices for each region of the most individually and obtain a semantic sampling ratio distribution map shared with the receiver. Through the guidance of the map, high-quality reconstruction is achieved. Meanwhile, attention-based channel adaptive module (ACAM) is designed to overcome the neural network model mismatch between the training and testing channel environment during sampling-reconstruction and encoding-decoding. To this end, signal-to-noise ratio (SNR) is employed as an extra parameter input to integrate and reorganize intermediate characteristics. Simulation results show that the proposed Adaptive-JSSCC effectively reduces the amount of data acquisition without degrading the reconstruction performance in comparison to the state-of-the-art, and it is highly adaptable and adjustable to dynamic channel environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel coding

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Resource Allocation for Emergency Communications in Distribution network Satellite Communication System

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China Communications 2025年第2期22卷 95-111页

作者： Xie Haoran Zhan Yafeng Fang Xin Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China State Key Laboratory of Space Network and Communications Tsinghua UniversityBeijing 100084China State Grid Jiangsu Electric Power Co. Ltd.Research InstituteNanjing 210000Jiangsu ProvinceChina

Frequent extreme disasters have led to frequent large-scale power outages in recent *** quickly restore power,it is necessary to understand the damage information of the distribution network ***,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution *** this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution ***,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation ***,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite ***,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed *** results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.

关键词： distribution network emergency communication resource scheduling satellite communication

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Graph Representation Learning for Microarchitecture Design Space Exploration 23

Graph Representation Learning for Microarchitecture Design S...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and School of Microelectronics Fudan University Shanghai China ZTE Corporation China and State Key Laboratory of Mobile Network and Mobile Multimedia Technology China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRL-DSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

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Gap dynamics in the *** urban areas in the current trend and in sustainable scenario

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Geography and Sustainability 2025年第1期6卷 143-158页

作者： Haoyu Wang Xiuyuan Zhang Shihong Du Yuyu Zhou Donghai Wu Qian Wang Lubin Bai Bo Liu Shuping Xiong College of Urban and Environmental Sciences Peking UniversityBeijing 100871China Institute of Remote Sensing and GIS Peking UniversityBeijing 100871China Department of Geography The University of Hong KongHong Kong SAR 999077China Institute for Climate and Carbon Neutrality The University of Hong KongHong Kong SAR 999077China Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China State Key Laboratory of Remote Sensing Science Beijing Normal UniversityBeijing 100875China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190China Key Laboratory of Network Information System Technology(NIST) Institute of ElectronicsChinese Academy of SciencesBeijing 100190China

The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable ***,there is a paucity of knowledge on this cutting-edge *** the extensive and rapid urbanization in the United states(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global *** study finds that although the 740 *** have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that *** study also reveals the spatio-temporal heterogeneity of the *** gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city *** cities in the Northwestern *** UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of *** the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of ***,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the *** globally.

关键词： Urban areas in the current trend Urban areas in the sustainable scenario Urban gap dynamics Urban sustainability

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LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO Satellite networks

LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO S...

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IEEE Conference on Wireless Communications and networking

作者： Shiran Sun Ran Zhang Kai Liu Zekun Sun Qinqin Tang Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China State Key Laboratory of Space Network and Communications Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of Software Defined networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

关键词： Satellites network topology Software algorithms Low earth orbit satellites Quality of service Jitter Routing Delays Topology Software defined networking

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Sensing-Communication-Computation Integration for Federated Edge Learning with Controllable Model Dropout

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IEEE Internet of Things Journal 2025年

作者： Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The CAS Key Laboratory of Wireless Optical Communication University of Science and Technology of China Hefei China Network Intelligence Center School of Information Science and Technology ShanghaiTech University Shanghai China

FEderated Edge Learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: sensing, computation, and communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into Integrated Sensing-Computation-Communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a sub-model through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables such as batch size, sensing power, dropout rate, and communication power. This non-convex problem is decomposed into two sub-problems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes. © 2014 IEEE.

关键词： Federated learning

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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