2022 International Conference on Information Processing and Network Provisioning (ICIPNP)最新文献

Research on 5G Voice Solutions and Continuity Analysis 5G语音解决方案研究及连续性分析

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00037

Han Guo, J. Hou, Xiaohan Xu, Chunlei Hu, Ming Wei, Bowen Cai

引用次数: 0

Distributed AI-native Architecture for 6G Networks 6G网络分布式ai原生架构

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00019

Pengyu Li, Yanxia Xing, Wei Li

引用次数: 0

Hierarchical Association Features Learning for Network Traffic Recognition 基于层次关联特征学习的网络流量识别

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00035

Peng Xiang, Chengwei Peng, Qingshan Li

{"title":"Hierarchical Association Features Learning for Network Traffic Recognition","authors":"Peng Xiang, Chengwei Peng, Qingshan Li","doi":"10.1109/ICIPNP57450.2022.00035","DOIUrl":"https://doi.org/10.1109/ICIPNP57450.2022.00035","url":null,"abstract":"With the development of network technology, identifying specific traffic has become important in network monitoring and security. However, designing feature sets that can accurately describe network traffic is still an urgent problem. Most of existing researches cannot realize effectively the identification of targets, and don't perform well in the complex and dynamic network environment. Aiming at these problems, we propose a novel method in this paper, which learns correlation features of network traffic based on the hierarchical structure. Firstly, the method learns the spatial-temporal features using convolutional neural networks (CNNs) and the bidirectional long short-term memory networks (Bi-LSTMs), then builds network topology to capture dependency characteristics between sessions and learns the context-related features through the graph attention networks (GATs). Finally, the network traffic session is classified using a fully connected network. The experimental results show that our method can effectively improve the detection ability and achieve a better classification performance overall.","PeriodicalId":231493,"journal":{"name":"2022 International Conference on Information Processing and Network Provisioning (ICIPNP)","volume":"65 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120899247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preface ICIPNP 2022 序言ICIPNP 2022

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/icipnp57450.2022.00005

引用次数: 0

Technical Solutions to Resolve Deterministic Requirements in the Industrial Site 解决工业现场确定性需求的技术解决方案

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00028

Jinyan Li, Na Lu, Cheng Li, Yanxia Xing, Wei Li

引用次数: 0

Leveraging Graph Attention Network for SDN Routing Performance Prediction 利用图关注网络进行SDN路由性能预测

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00010

Yonghua Huo, Y. Liu, Shilong Zhao, Peng Yu

{"title":"Leveraging Graph Attention Network for SDN Routing Performance Prediction","authors":"Yonghua Huo, Y. Liu, Shilong Zhao, Peng Yu","doi":"10.1109/ICIPNP57450.2022.00010","DOIUrl":"https://doi.org/10.1109/ICIPNP57450.2022.00010","url":null,"abstract":"With the continuous expansion of the network scale, the network topology is becoming increasingly complex, and the link status between network devices will also change dynamically according to real-time load, link quality and other factors, increasing the difficulty of solving network optimization problems. Therefore, an efficient and fine-grained network model is essential to achieve the goal of efficient and autonomous network optimization. As the development direction of future network architecture, Software Defined Networking (SDN) technology can effectively set up routing schemes and flexibly control network traffic by separating data plane and control plane. In the process of routing scheme optimization, the key is to accurately estimate the network performance under a given routing scheme. In this paper, we propose a SDN routing performance prediction model based on graph attention network. Based on graph attention network, this model models the relationship between physical links and routing scheme paths in the network. Under the given routing scheme and network traffic, it accurately estimates each end-to-end performance index in the network to assist in optimizing the routing scheme.","PeriodicalId":231493,"journal":{"name":"2022 International Conference on Information Processing and Network Provisioning (ICIPNP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125124236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on evaluation model and technology of 5G core network User Plane Function 5G核心网用户平面功能评估模型与技术研究

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00036

Jiannan Ou, J. Yin, Jie Chen

引用次数: 0

DFCNI: Researching on Data-centric Framework of Cloud Network Integration for Telecom Operators DFCNI:电信运营商以数据为中心的云网络集成框架研究

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00017

Xinchi Li, Xiaoqing Xia, Hongpeng Chu, Jun Zhao, Hui Yan

{"title":"DFCNI: Researching on Data-centric Framework of Cloud Network Integration for Telecom Operators","authors":"Xinchi Li, Xiaoqing Xia, Hongpeng Chu, Jun Zhao, Hui Yan","doi":"10.1109/ICIPNP57450.2022.00017","DOIUrl":"https://doi.org/10.1109/ICIPNP57450.2022.00017","url":null,"abstract":"With the rapid development of network technology and cloud computing, the data of the telecommunications industry has shown massive growth. Traditionally, telecom operators have built integrated information services with cloud and network infrastructure as the core. But data remains isolated and underutilized. The overall big data architecture and infrastructure that oriented data value circulation and integrated multiple data, technologies and resources have not really been established. So we proposes a DFCNI: Data-centric Framework of Cloud Network Integration for Telecom Operators, which can provide an innovative infrastructure for telecom operators' big data industry. We first analyzed the technical architecture, deployment scheme and key technologies of DFCNI. This architecture changes the traditional cloud resource concentration mode, and makes full use of the resource capabilities of the cloud and network through distributed computing, resource migration, virtualization and other technologies. And then we introduced a typical application scenario of DFCNI- -Smart City, Solving the problem of multi-party data fusion in this scenario under the premise of ensuring data privacy. Finally, we summarized three advantages of DFCNI and proposed future research directions including data right confirmation technology and data circulation guarantee technology based on Blockchain.","PeriodicalId":231493,"journal":{"name":"2022 International Conference on Information Processing and Network Provisioning (ICIPNP)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130626425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polarimetric Radar Target Classification Based on Decision Tree 基于决策树的极化雷达目标分类

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00034

Yanhan Li, Jingcheng Zhao, Zongkai Yang, Ke Zhang

{"title":"Polarimetric Radar Target Classification Based on Decision Tree","authors":"Yanhan Li, Jingcheng Zhao, Zongkai Yang, Ke Zhang","doi":"10.1109/ICIPNP57450.2022.00034","DOIUrl":"https://doi.org/10.1109/ICIPNP57450.2022.00034","url":null,"abstract":"Due to advances in science and technology, military weaponry have been progressively improved, the current battlefield has grown more complex, and the issue of identifying aircraft targets in air warfare has gained more attention. Over time, operational needs have become more unmet by conventional radars. To successfully conduct military operations, radars that can offer more comprehensive target information are urgently required. Target classification and identification can be accomplished by using polarization radar, relying on polarization features to augment the information of the irradiation target in the polarization dimension, paired with the decision tree approach of machine learning. In this study, two scaled models of airplanes with various military functions are created and put through simulations before having their polarization characteristics described using the Poincare sphere. To perform the classification and identification of the two types of aircraft models, a classification decision tree is built and combined with the CART method. The simulation results demonstrate that the polarized feature information of the decision tree has a 94% success rate for target categorization based on the CART algorithm.","PeriodicalId":231493,"journal":{"name":"2022 International Conference on Information Processing and Network Provisioning (ICIPNP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coexistence Analysis of 1800MHz Band Civil UAV System and TDD Private Network System 1800MHz频段民用无人机系统与TDD专网系统共存分析

2022 International Conference on Information Processing and Network Provisioning (ICIPNP) Pub Date : 2022-09-01 DOI: 10.1109/ICIPNP57450.2022.00008

Lei Liu, Bingxin Wang, M. Kadoch

引用次数: 0