2022 27th Asia Pacific Conference on Communications (APCC)最新文献_第9页

Accuracy of Natural Language Processing Algorithms in Representing Stressed Words in Closed Captioning 自然语言处理算法在封闭字幕重音表示中的准确性

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943727

Prawit Boonmee, Prajaks Jitngernmadan, Kanuengnij Kubola, Kemtong Sinwongsuwat, P. Prempraneerach

引用次数: 0

KID: Knowledge Graph-Enabled Intent-Driven Network with Digital Twin 基于数字孪生的知识图驱动的意图驱动网络

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943779

Xiaotian Chang, Chungang Yang, Hao Wang, Ying Ouyang, Ru Dong, Junjie Guo, Zeyang Ji, Xianglin Liu

{"title":"KID: Knowledge Graph-Enabled Intent-Driven Network with Digital Twin","authors":"Xiaotian Chang, Chungang Yang, Hao Wang, Ying Ouyang, Ru Dong, Junjie Guo, Zeyang Ji, Xianglin Liu","doi":"10.1109/APCC55198.2022.9943779","DOIUrl":"https://doi.org/10.1109/APCC55198.2022.9943779","url":null,"abstract":"To meet novel services and networking requirements towards the next generation applications, intent-driven network is proposed as a promising networking paradigm. It is with capabilities of intent refinement, policy generation, and state awareness. And these distinctive capabilities contribute to its wide applications to the next generation networks. However, current researches lack a generalization model of intent refinement. Additionally, it is difficult to extract available knowledge from huge raw data of the network status, and guarantee the precise generation of network policies. To solve these challenges, we present a knowledge graph-enabled intent-driven network with the digital twin, which is termed as KID in this work. In the KID, knowledge graph is utilized to represent user intents, abstract network status, and express network policies. And the digital twin is applied to validate intents as well as abstract the physical network. The KID enhances the capabilities of intent-driven networks to refine intents, contributing to the continuous assurance of accurate intent fulfillment. Finally, we present a proof of concept implementation of the KID. Simulation results verify the feasibility and effectiveness of the presented KID framework.","PeriodicalId":403546,"journal":{"name":"2022 27th Asia Pacific Conference on Communications (APCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125404690","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: 4

Image Encryption Based on 5-Neighbor Programmable Cellular Automata 基于5邻可编程元胞自动机的图像加密

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943590

S. Kang, U. Choi

引用次数: 0

Double-Machine-Learning-Based Resource Scheduling Method for Offloading Transfers 基于双机器学习的分流资源调度方法

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943560

Xiao Lin, Song-Hui Lin, Yaping Li, Junyi Shao, Keqin Shi, Yajie Li

{"title":"Double-Machine-Learning-Based Resource Scheduling Method for Offloading Transfers","authors":"Xiao Lin, Song-Hui Lin, Yaping Li, Junyi Shao, Keqin Shi, Yajie Li","doi":"10.1109/APCC55198.2022.9943560","DOIUrl":"https://doi.org/10.1109/APCC55198.2022.9943560","url":null,"abstract":"Dynamically scheduling the bandwidth based on the traffic variation is important for a task offloading system. However, it faces two challenges. On one hand, the time-varying nature of the offloading traffic makes it difficult to be predicted accurately. On the other hand, differentiated mechanisms are applied to different offloading task types, which greatly complicates the behavior of the task offloading system. It is hence difficult to estimate the performance metrics accurately, especially when the metric values are extremely small. To tackle this, we present a double-machine-learning-based resource scheduling (DML-RS) method for task offloading traffic in this paper. The features of DML-RS are as follows: i) the wavelet transform and the sliding time window are incorporated with the LSTM traffic prediction model, which can capture the periodic and volatile natures of the offloading traffic and hence improve the prediction accuracy; ii) the logarithmic converting is applied to the ANN estimation models, which can improve the sensitivity of the ANN models to the small values and hence provides higher estimation accuracy. As a result, DML-RS can predict the traffic demand of the next network reconfiguration time point and optimize the resource allocation based on the performance estimations in advance. Results show that DML-RS offers near-optimal results compared with the existing method.","PeriodicalId":403546,"journal":{"name":"2022 27th Asia Pacific Conference on Communications (APCC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124125014","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

Efficient UAV/Satellite-assisted IoT Task Offloading: A Multi-agent Reinforcement Learning Solution 高效无人机/卫星辅助物联网任务卸载:多智能体强化学习解决方案

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943714

Kangjia Yu, Qimei Cui, Ziyuan Zhang, Xueqing Huang, Xuefei Zhang, Xiaofeng Tao

{"title":"Efficient UAV/Satellite-assisted IoT Task Offloading: A Multi-agent Reinforcement Learning Solution","authors":"Kangjia Yu, Qimei Cui, Ziyuan Zhang, Xueqing Huang, Xuefei Zhang, Xiaofeng Tao","doi":"10.1109/APCC55198.2022.9943714","DOIUrl":"https://doi.org/10.1109/APCC55198.2022.9943714","url":null,"abstract":"In the future mobile edge networks, the Internet of things (IoT) applications will be latency-sensitive and computationally intensive. Given the resource limitation of IoT devices, mobile edge computing (MEC) servers are critical to support the efficient processing of IoT tasks. Since MEC servers attached to the ground base stations are generally deployed in fixed locations and vulnerable to physical damage, the unmanned aerial vehicle (UAV) and satellite-assisted MEC framework has been proposed to leverage the flexibility of UAVs and the broad coverage of satellites. However, efficient utilization of the UAV/satellite resources is challenging for the static ground IoT devices because of the dynamic in terms of aerial and space network topology and IoT task arrival rates. To adapt to the changing environment and utilize the interaction among multiple UAVs, we propose a multi-agent deep deterministic policy gradient (MADDPG) framework to jointly optimize the traveling routes of multi-UAVs and the offloading decision of IoT devices. To minimize the processing cost in terms of task processing latency and energy consumption of IoT devices, cooperative UAVs can help find the optimal task offloading location for each IoT device. Simulation results show the proposed algorithm based on MADDPG can averagely decrease 20% of the above processing cost compared with the benchmark approach.","PeriodicalId":403546,"journal":{"name":"2022 27th Asia Pacific Conference on Communications (APCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761010","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: 2

Covariance-Based Time-Frequency ESPRIT Algorithm for Direction-of-Arrival Estimation 基于协方差的时频ESPRIT到达方向估计算法

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943741

Seungnyun Kim, Jiao Wu, Ahnho Lee, Yiying Liu, Yongseok Byun, B. Shim

引用次数: 0

A lightweight Machine-learning based Wireless Link Estimation for IoT devices 基于轻量级机器学习的物联网设备无线链路估计

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943633

Khanh-Hoi Le Minh, Kim-Hung Le, Q. Trung

引用次数: 0

An Overview of Architecture and Applications of Delay Tolerant Network for Korea Pathfinder Lunar Orbiter 韩国探路者月球轨道器容延迟网络结构与应用综述

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943687

Kyung-Rak Lee, Jin-Ho Jo, Byoung-Sun Lee, Sinae Ji

引用次数: 1

Analysis of SINR according to Elevation Angle in Earth Fixed Beam 基于仰角的大地固定波束信噪比分析

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943773

Jin Yu, Jae-Hyun Kim

引用次数: 0

Optical Signals and Prospects Towards Multimedia in Underwater Wireless and Mobile Communication 水下无线和移动通信中的光信号及其多媒体发展前景

2022 27th Asia Pacific Conference on Communications (APCC) Pub Date : 2022-10-19 DOI: 10.1109/APCC55198.2022.9943699

Tharuka Jayadewa Govinda Waduge, A. Al-Anbuky, K. Vopel

引用次数: 0