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Distributed Routing and Data Scheduling in IPNs With GNN-Based Multi-Agent DRL

IEEE Internet of Things Journal 2025年

作者： Zhou, Xixuan Xu, Ruoyu Tian, Xiaojian Zhang, Yueyue Liang, Yu Chen, Xiaoliang Zhu, Zuqing University of Science and Technology of China School of Information Science and Technology Anhui Hefei230027 China Eastview Secondary School BarrieON Canada Shanghai Satellite Network Research Institute Shanghai Key Lab of Satellite Network Shanghai200120 China

As deep space exploration missions grow in complexity, efficient data transfer in interplanetary networks (IPNs) becomes paramount. However, the vast distances, limited bandwidth, and dynamic nature of IPNs pose significant challenges for the routing and data scheduling of interplanetary data transfers (IP-DTs). To address these challenges, this work proposes a novel distributed, graph neural network (GNN) based multi-agent deep reinforcement learning (DRL) approach that can jointly optimize the routing and scheduling of IP-DTs. Our proposal is based on the proximal policy optimization (PPO) framework along with the graph attention networks (GATs). We make the DRL agents for IPN nodes in each subnetwork around a celestial body learn and operate independently, for making intelligent routing and scheduling decisions to properly trade off between average end-to-end (E2E) latency and delivery ratio of IP-DTs while ensuring good scalability. Extensive simulations confirm that our proposal handles the routing and scheduling of IP-DTs much better than existing benchmarks. Further, by modifying the interplanetary overlay network (ION) software platform developed by NASA, we build a semi-physical IPN emulator based on Raspberry Pi boards, implement our proposal in it, and conduct experiments with real data transfers between IPN nodes. Experimental results verify that our proposal can work for practical IPNs without causing excessive overheads and prove its advantages. © 2025 IEEE.

关键词： NASA

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite networks

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China Communications 2022年第4期19卷 230-243页

作者： Qingqing Tang Zesong Fei Bin Li School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

关键词： LEO satellite networks computation offloading deep neural networks

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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A Digitally assisted nonlinearity suppression architecture using over-the-air beam cancelation

A Digitally assisted nonlinearity suppression architecture u...

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作者： Mu, Qin Li, Nanxi Chen, Cong Wei, Xiaozheng Wen, Rong Wu, Muqing Li, Xiang Liu, Ying School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China National Key Lab of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two-step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in-band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion. © 2023 John Wiley & Sons Ltd.

关键词： Power amplifiers

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On the Capacity of Citation Generation by Large Language Models

arXiv

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arXiv 2024年

作者： Qian, Haosheng Fan, Yixing Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external traceable resources for response generation. The essence of RAG in combating the hallucination issue lies in accurately attributing claims in responses to the corresponding retrieved documents. However, most of existing works focus on improving the quality of generated responses from the LLM, while largely overlooked its ability to attribute sources accurately. In this study, we conduct a systematic analysis about the capabilities of LLMs in generating citations within response generation, and further introduce a novel method to enhance their citation generation abilities. Specifically, we evaluate both the correctness and citation quality for seven widely-used LLMs on two benchmark datasets. Meanwhile, we introduce new citation evaluation metrics to eliminate the over-penalization of unnecessary and excessive citations in existing metrics. Furthermore, we propose a Generate-then-Refine method that completes relevant citations and removes irrelevant ones without altering the response text. The results on WebGLM-QA, ASQA and ELI5 datasets show that our method substantially improves the quality of citations in responses generated by LLMs. Copyright © 2024, The Authors. All rights reserved.

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Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective

arXiv

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arXiv 2025年

作者： Li, Da Bi, Keping Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China

Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (including titles, headers, and cells) present unique challenges. For example, different table fields have varying matching preferences: cells may favor finer-grained (word/phrase level) matching over broader (sentence/passage level) matching due to their fragmented and detailed nature, unlike titles. This necessitates a table-specific retriever to accommodate the various matching needs of each table field. Therefore, we introduce a Table-tailored HYbrid Matching rEtriever (THYME), which approaches table retrieval from a field-aware hybrid matching perspective. Empirical results on two table retrieval benchmarks, NQ-TABLES and OTT-QA, show that THYME significantly outperforms state-of-the-art baselines. Comprehensive analyses confirm the differing matching preferences across table fields and validate the design of THYME. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

arXiv

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arXiv 2025年

作者： Ni, Shiyu Bi, Keping Guo, Jiafeng Yu, Lulu Bi, Baolong Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China

Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries, leading to confident yet incorrect responses. This paper explores leveraging LLMs’ internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives. We investigate whether LLMs can estimate their confidence using internal states before response generation, potentially saving computational resources. Our experiments on datasets like Natural Questions, HotpotQA, and MMLU reveal that LLMs demonstrate significant pre-generation perception, which is further refined post-generation, with perception gaps remaining stable across varying conditions. To mitigate risks in critical domains, we introduce Consistency-based Confidence Calibration (C3), which assesses confidence consistency through question reformulation. C3 significantly improves LLMs’ ability to recognize their knowledge gaps, enhancing the unknown perception rate by 5.6% on NQ and 4.9% on HotpotQA. Our findings suggest that pre-generation confidence estimation can optimize efficiency, while C3 effectively controls output risks, advancing the reliability of LLMs in practical applications © 2025, CC BY.

关键词： Depth perception

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Algorithms of Time Series network: Approaches Reproduction and networks Topology 9

Algorithms of Time Series Network: Approaches Reproduction a...

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Wang, Li-Na Cheng, Yuan-Yuan Tan, Gui-Min Science College Inner Mongolia University of Technology Hohhot010051 China Inner Mongolian Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot010051 China

ISBN: (纸本)9781643684703

network analysis methods of time series provide a new analytical framework on describing complex behaviors using sample data. Reproducibility is one of the important principles in scientific research. This work focuses on the algorithm review and reproduction of time series network. We paid special attention to the applicability and data characteristics of the five typical algorithms: visibility graph, phase space reconstruction, fluctuation mode, symbolic representation and coarsened multidimensional time series. It provides a reference and inspiration for future analysis of time series with various characteristics. Although two pioneer methods are widely applicable, while the directed weighted network established by coarsening thoughts contains more information about time series. In addition, coarseness process makes these approaches perform better for massive data analysis. It should be noted that: the process of data coarsening needs to ensure that the data characteristics of the original time series are inherited. © 2023 The authors and IOS Press.

关键词： Time series

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A Caching Strategy Based on Many-to-Many Matching Game in D2D networks

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Tsinghua science and technology 2021年第6期26卷 857-868页

作者： Kaihang Yu Zhonggui Ma Runyu Ni Tao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Lab of Information Network Security Ministry of Public SecurityShanghai 201204China

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device(D2D) networks. We model the D2D User Equipments(DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy.(1) It can cache other contents besides high popularity contents.(2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used(LRU), Equal Probability Random Cache(EPRC), and the Most Popular Content Cache(MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

关键词： caching strategy Device-to-Device(D2D)networks Gauss determinantal point process many-to-many matching game

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