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Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning Based Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Dan Zhu, Keke Song, Bin Yu, F. Richard Xidian University State Key Laboratory of Integrated Services Networks 710071 China Carleton University Department of Systems and Computer Engineering Ottawa Canada

The explosive growth in video services has significantly strained current mobile network infrastructure, leading to spectrum scarcity, backhaul congestion, and degraded quality of experience. While edge caching has emerged as a promising solution to address these challenges and deliver seamless video playback experience, multi-version edge caching for heterogeneous clients remains challenging due to varying client requirements and network conditions. This paper proposes a transcoding enabled edge caching framework for mobile edge-cloud computing networks. Specifically, we combine video transcoding with edge caching to support both "direct cache hits" and "soft cache hits" for reducing transmission latency. We model this joint caching and resource allocation problem as a Stackelberg game to minimize video transmission latency. To solve this problem, we develop a novel dual timescale Model Agnostic Meta-Learning (MAML) based Stackelberg game (DTMSG) optimization approach that determines the delay-optimal Stackelberg equilibrium (SE) and accelerates convergence. Simulation results demonstrate that our DTMSG optimization algorithm efficiently converges to the SE point, maximizing the utility function of the MVNO and BSs while reducing the average video transmission delay. © 2014 IEEE.

关键词： Mobile edge computing

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Verifier-local revocation group signatures with backward unlinkability from lattices

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Frontiers of Information Technology & Electronic engineering 2022年第6期23卷 876-892页

作者： Yanhua ZHANG Ximeng LIU Yupu HU Yong GAN Huiwen JIA College of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou 450001China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Stale Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China College of Information Engineering Zhengzhou University of TechnologyZhengzhou 450044China School of Mathematics and Information Science Guangzhou UniversityGuangzhou 510006China

For group signature(GS)supporting membership revocation,verifier-local revocation(VLR)mechanism seems to be a more flexible choice,because it requires only that verifiers download up-to-date revocation information for signature verification,and the signers are not *** a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions,the first lattice-based VLR group signature(VLR-GS)was introduced by Langlois et al.(2014).However,none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability(BU),which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer(i.e.,member)is *** this study,we introduce the first lattice-based VLR-GS scheme with BU security(VLR-GS-BU),and thus resolve a prominent open problem posed by previous *** new scheme enjoys an O(log N)factor saving for bit-sizes of the group public-key(GPK)and the member’s signing secret-key,and it is free of any public-key *** the random oracle model,our scheme is proven secure under two well-known hardness assumptions of the short integer solution(SIS)problem and learning with errors(LWE)problem.

关键词： Group signature Lattice-based cryptography Verifier-local revocation Backward unlikability Short integer solution

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Multi-ID2R: An Intelligent Device Disaster Recovery Mechanism in Multipath Scenarios

Multi-ID2R: An Intelligent Device Disaster Recovery Mechanis...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Wenxiao Zhang, Yuyang Dong, Ping Du, Xiaojiang Yu, Chengxiao Zhang, Hongke Aitsaadi, Nadjib Beijing Jiaotong University School of Electronic and Information Engineering China Pengcheng Laboratory Dept. of New Networks Shenzhen China Stevens Institute of Technology Dept. of Electrical and Computer Engineering HobokenNJ United States Université Paris-Saclay Uvsq David Versailles France

ISBN: (纸本)9798350351255

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks. © 2024 IEEE.

关键词： Bandwidth

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A structure based on fiber-optic gyroscopes for gravity measurement 2

A structure based on fiber-optic gyroscopes for gravity meas...

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Advanced Optical Manufacturing Technologies and Applications 2022, AOMTA 2022 and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2022

作者： Chen, Yanjun Zhu, Lanxin Wang, Wenbo He, Yan Cao, Yuwen Huang, Huimin Ma, Xiangdong Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronics Engineering & Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510661264

We propose a structure based on fiber-optic gyroscopes for gravity measurement in accordance with the tilt-coupling effect, where Sagnac effect can remove the coupling between translational and rotational motion in the tilt-coupling effect. A fiber-optic gyroscope with a sensitivity of 2 × 10−11 rad/s2/√Hz in sub-millihertz frequencies is reported, which can be utilized to measure the rotational motion in gravity measurement. The application of dual-polarization configuration improves the adaptability of exploration environment. © 2023 SPIE. All rights reserved.

关键词： Fiber optics

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LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of Communications and Information networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

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GrayINT – Detection and Localization of Gray Failures via Hybrid In-band Network Telemetry 24

GrayINT – Detection and Localization of Gray Failures via H...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Zhang, Kuichao Su, Wei Shi, Huiling Zhang, Kai Zhang, Wei Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9788995004395

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band Network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization. Copyright 2023 KICS.

关键词： Next generation networks

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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