2022 21st International Symposium on Communications and Information Technologies (ISCIT)最新文献

Tri-Branch GAN: A Semi-supervised Method Based on Rebalance 三分支GAN:基于再平衡的半监督方法

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931307

Weiqiang Zhong, Tiankui Zhang, Yapeng Wang, Zeren Chen

引用次数: 0

Performance Analysis of ADS-B Receiving System Based on LEO Satellite Constellation 基于LEO卫星星座的ADS-B接收系统性能分析

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931245

Xiaofeng Zhu, Xiongfei Li, Xianfeng Gong

引用次数: 4

Proposal of Single Input BSS Method Using QMF for Pulse Wave Extraction 基于QMF的单输入BSS脉冲波提取方法的提出

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931304

Seiya Ogawa, Minoru Komatsu, H. Matsumoto

引用次数: 0

Beidou Carrier Phase Difference Reliability Improvement Method Applied to Medium and Large Fixed Wing UAV in Complex Environment 应用于复杂环境下大中型固定翼无人机的北斗载波相位差可靠性改进方法

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931231

Qi-yi Ren, Xiang Wan, Jiajia Wu

引用次数: 0

Initial Implementation and Utilization of Instruction Dynamic Simulation in MEIMAT 教学动态仿真在MEIMAT中的初步实现与应用

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931212

Tadahiro Ogita, Tomoyuki Morimoto, Shunsuke Onogi, Ryota Kawamata, Ziming Wang, Toshiyuki Tsutsumi

引用次数: 0

Speech Enhancement with Fractional Fourier Transform 分数阶傅里叶变换的语音增强

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931218

Cun Zhu, Yan Sun, Chun-zhi Pan

引用次数: 0

Task Offloading Strategies for Cloud-Side Cooperation in Compute-Intensive Scenarios Based on Edge Computing 计算密集型场景下基于边缘计算的云端协作任务卸载策略

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931296

Wei Han, Jianan Su, Siting Lv, Peng Zhang, Xiaohui Li

{"title":"Task Offloading Strategies for Cloud-Side Cooperation in Compute-Intensive Scenarios Based on Edge Computing","authors":"Wei Han, Jianan Su, Siting Lv, Peng Zhang, Xiaohui Li","doi":"10.1109/ISCIT55906.2022.9931296","DOIUrl":"https://doi.org/10.1109/ISCIT55906.2022.9931296","url":null,"abstract":"In computationally intensive scenarios, a large number of the terminal device is accessed locally. In order for the service to complete, the device needs to complete their tasks within the short time period set by the developers. Mobile Edge Computing (MEC) is an emerging technology deployed at the edge network of devices such as terminals and base stations, which can effectively meet the needs of computation-intensive scenarios. In this paper, we study a task offloading strategy based on edge computing to solve computationally intensive scenarios. We propose a collaborative cloud-edge task offloading model based on the framework of Mobile Device (MD), Edge Node (EN), and Cloud Computing (CC). Then we analyze the communication and computational requirements of the model and propose a linear offloading solution idea. We leverage this idea to propose a scheme to minimize the overall delay in the scene. The problem of the model is transformed into a non-convex problem by transforming it into a convex optimization thus solving the optimal offloading decision. Simulation results show that the scheme can effectively solve the delay optimization problem for computationally intensive scenarios.","PeriodicalId":325919,"journal":{"name":"2022 21st International Symposium on Communications and Information Technologies (ISCIT)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132954171","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

A Multi-level Trusted Clustering Mechanism for IOT 物联网多层次可信集群机制

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931247

Yaxin Zhao, Chunzi Chen

引用次数: 0

Fool Your Enemies: Enable Cyber Deception and Moving Target Defense for Intrusion Detection in SDN 欺骗你的敌人:启用网络欺骗和移动目标防御入侵检测在SDN

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931208

Phan The Duy, Hien Do Hoang, Nghi Hoang Khoa, Do Thi Thu Hien, V. Pham

{"title":"Fool Your Enemies: Enable Cyber Deception and Moving Target Defense for Intrusion Detection in SDN","authors":"Phan The Duy, Hien Do Hoang, Nghi Hoang Khoa, Do Thi Thu Hien, V. Pham","doi":"10.1109/ISCIT55906.2022.9931208","DOIUrl":"https://doi.org/10.1109/ISCIT55906.2022.9931208","url":null,"abstract":"The adoption of deception technology constructed to throw off stealthy attackers from real assets and gather intelligence about how they operate is gaining ground in the network system. Also, some static honeypots are deployed in the network system to attract adversaries for avoiding them accessing the real targets. This leads to a disclosure of the existence of cyber traps in the network that do not fool skillful attackers. Meanwhile, there are many intrusion detection systems (IDS) lack the abnormal traffic sample to obtain the knowledge of cyberattacks. Hence, it is vital to make honeypots more dynamically and give the material for harvesting useful threat intelligence for detector. Taking advantage of Software Defined Networking (SDN), cyber traps can be easily deployed when an intrusion detector triggers or actively laid in advance to mitigate the impact of adversaries into real assets. Instead of building IDS separately or blocking attacks promptly after an alert issued, in this paper, we utilize the strategy of associating Cyber Deception, and Moving Target Defense (MTD) with IDS in SDN, named FoolYE (Fool your enemies) to slow a network intruder down and leverage the behaviors of adversaries on traps for feeding back detector awareness.","PeriodicalId":325919,"journal":{"name":"2022 21st International Symposium on Communications and Information Technologies (ISCIT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121849798","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: 1

Deep Learning-Based Variable Scaling Beam Training for Massive MIMO mmWave Systems 基于深度学习的大规模MIMO毫米波系统变尺度波束训练

2022 21st International Symposium on Communications and Information Technologies (ISCIT) Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931243

Zaoshi Wang, Na Chen, M. Okada

{"title":"Deep Learning-Based Variable Scaling Beam Training for Massive MIMO mmWave Systems","authors":"Zaoshi Wang, Na Chen, M. Okada","doi":"10.1109/ISCIT55906.2022.9931243","DOIUrl":"https://doi.org/10.1109/ISCIT55906.2022.9931243","url":null,"abstract":"Improving the accuracy of beam training while reducing the training overhead and the influence of noise has become an important issue for massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) communication systems. In this paper, we propose a deep learning-based multi-scale neural network for beam training in massive MIMO mmWave communication system. The model predicts the orientation of narrow beams by learning the characteristics of wide beams, achieving high accuracy and low training overhead. Specifically, our model consists of three modules. In the first module, we deploy a convolutional neural network (CNN) to extract features of the instantaneous received signal of a wide beam. In the second module, we develop multi-scale convolution to extract wide beam features of the different time combination. In the third module, we conduct a long-term short-term memory (LSTM) network to calibrate the orientation of narrow beams based on previous predictions, thereby enhance the model's robustness to noise. Finally, according to the experimental results, our model improves beam training accuracy with low training overhead while reducing the influence of noise.","PeriodicalId":325919,"journal":{"name":"2022 21st International Symposium on Communications and Information Technologies (ISCIT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133709464","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