2021 IEEE Globecom Workshops (GC Wkshps)最新文献_第6页

Random Access Preamble Design for 3GPP Non-terrestrial Networks 3GPP非地面网络随机接入前导设计

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681944

T. Khan, Xingqin Lin

引用次数: 4

Artificial Intelligence Enabled Self-healing for Mobile Network Automation 人工智能支持移动网络自动化的自修复

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681937

M. Asghar, F. Ahmed, Jyri Hämäläinen

{"title":"Artificial Intelligence Enabled Self-healing for Mobile Network Automation","authors":"M. Asghar, F. Ahmed, Jyri Hämäläinen","doi":"10.1109/GCWkshps52748.2021.9681937","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681937","url":null,"abstract":"This paper presents an artificial intelligence enabled self-healing framework for cell outage detection and compensation in radio access networks. The developed framework consists of three modules, namely cell outage detection, cell outage compensation, and continuous optimization that work in closed-loop to detect outages, trigger recovery actions, and network optimization to minimize the impact of outages on user experience. The outage detection module is based on machine learning algorithms aimed to detect anomalies in the network performance data. Likewise, the cell outage compensation module uses fuzzy logic to determine compensation actions after an outage cell has been detected. The continuous optimization module is tasked with making incremental improvements to the network configuration through a heuristic approach. The developed self-healing framework is validated using a network simulator ns-3 based test environment. Results show the framework is capable of fully recovering from the outage in terms of accessibility and coverage. In addition, the cell edge reference signal received power is recovered by 45%, thereby significantly improving the network performance once the outage is detected.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86180220","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

Multi-interface network framework for UAV management and data communications 用于无人机管理和数据通信的多接口网络框架

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682019

Victor Sanchez-Agüero, M. Fas-Millán, F. Valera, I. Vidal, Alejandro Paniagua-Tineo, R. L. D. Silva, J. M. Manjón

引用次数: 1

Energy Efficiency Framework for Time-limited Contention in the IEEE 802.11ah Standard IEEE 802.11ah标准中限时竞争的能源效率框架

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681998

Hamid Taramit, L. Orozco-Barbosa, A. Haqiq

引用次数: 2

SeqNet: Data-Driven PAPR Reduction via Sequence Classification SeqNet:基于序列分类的数据驱动PAPR降低

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682102

Hyeondeok Jang, Seowoo Jang, Yosub Park, Jungsoo Jung, Juho Lee, Sunghyun Choi

{"title":"SeqNet: Data-Driven PAPR Reduction via Sequence Classification","authors":"Hyeondeok Jang, Seowoo Jang, Yosub Park, Jungsoo Jung, Juho Lee, Sunghyun Choi","doi":"10.1109/GCWkshps52748.2021.9682102","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682102","url":null,"abstract":"To fully exploit Terahertz spectrum for the upcoming 6G communications, peak-to-average power ratio (PAPR) reduction plays an important role to enhance power efficiency and extend communication coverage. In this paper, we address the PAPR reduction with a data-driven approach and propose a PAPR reduction neural network, SeqNet, with the aid of a deep learning technique. Inspired by the well-known selected mapping (SLM) scheme, SeqNet finds a phase sequence leading to low PAPR when multiplied to a given modulated symbol block. SeqNet classifies a modulated symbol block into one of phase sequences, which are designed not to affect BER (Bit Error Rate) performance. We also propose, Split-SeqNet which splits the symbol block into multiple segments and finds a phase sequence for each. We perform comparative study with various data-driven PAPR reduction approaches and simulation result shows that SeqNet achieves better PAPR performance than conventional auto-encoder-based scheme, tone-reservation based, DFT-s-OFDM, and OFDM by about 0.2 dB, 0.4 dB, 1.6 dB, and 3.4 dB, respectively, which can be converted into 4.3%, 8.7%, 40.1% and 104.4% improvement of the communication coverage.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"19 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78673482","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

A Machine Learning-based SDN Controller Framework for Drone Management 无人机管理中基于机器学习的SDN控制器框架

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682027

Abbas Yazdinejad, Elnaz Rabieinejad, A. Dehghantanha, R. Parizi, Gautam Srivastava

{"title":"A Machine Learning-based SDN Controller Framework for Drone Management","authors":"Abbas Yazdinejad, Elnaz Rabieinejad, A. Dehghantanha, R. Parizi, Gautam Srivastava","doi":"10.1109/GCWkshps52748.2021.9682027","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682027","url":null,"abstract":"With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based Software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone’s radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger Technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone’s type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone’s type.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"7 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88307068","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: 14

SISO Decoding of U-UV Codes U-UV码的SISO解码

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682136

Changyu Wu, Li Chen

引用次数: 0

Optimizing the Configuration of Intelligent Reflecting Surfaces using Deep Learning 利用深度学习优化智能反射面的配置

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682108

C. Sun, Navid Naderializadeh, M. Hashemi

引用次数: 0

Network AI Management & Orchestration: A Federated Multi-task Learning Case 网络人工智能管理与编排:一个联邦多任务学习案例

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681969

Rongpeng Li, Wenliang Liang, Chenghui Peng, Xueli An, Zhifeng Zhao, Honggang Zhang

引用次数: 2

Performance Analysis of Wireless Practical Byzantine Fault Tolerance Networks Using IEEE 802.11 基于IEEE 802.11的无线实用拜占庭容错网络性能分析

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682068

Ziyi Zhou, Oluwakayode Onireti, Lei Zhang, Muhammad Ali Imran

引用次数: 4