2021 IEEE Globecom Workshops (GC Wkshps)最新文献_第7页

Joint Deployment Design and Power Control for UAV-enabled Covert Communications 无人机隐蔽通信的联合部署设计和功率控制

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682010

Ziheng Zhang, Yuchen Zhang, Sa Xiao, Jianquan Wang, Wanbin Tang

引用次数: 2

AI-powered Infrastructures for Intelligence and Automation in Beyond-5G Systems 超5g系统中用于智能和自动化的人工智能基础设施

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682117

L. Militano, Anastasios Zafeiropoulos, Eleni Fotopoulou, R. Bruschi, C. Lombardo, Andy Edmonds, S. Papavassiliou

引用次数: 2

O-RAN AI/ML Workflow Implementation of Personalized Network Optimization via Reinforcement Learning 通过强化学习实现个性化网络优化的O-RAN AI/ML工作流

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681936

Hoejoo Lee, Youngcheol Jang, Juhwan Song, Hunje Yeon

{"title":"O-RAN AI/ML Workflow Implementation of Personalized Network Optimization via Reinforcement Learning","authors":"Hoejoo Lee, Youngcheol Jang, Juhwan Song, Hunje Yeon","doi":"10.1109/GCWkshps52748.2021.9681936","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681936","url":null,"abstract":"In this paper, we study AI-based RAN technology for 5G and 6G networks that are more complex and difficult to analyze than previous generations to make the network more intelligent. We implement a reference AI/ML workflow using RAN Intelligent Controller (RIC) by referring to the AI/ML workflow architecture of O-RAN. We focus on the establishment of an online training environment based on the RIC platform. We use various open-source platforms to serve the ML model as an inference service and to build a Machine Learning (ML) training pipeline for online training. We train our own Reinforcement Learning (RL) model which controls function parameters in Distributed Unit (DU) to maximize total cell throughput. After training the model with data from a specific cell, it is deployed in a different environment. We demonstrate the effectiveness of our proposal by optimizing the model performance and executing the training pipeline for retraining the model using online workflow. As compared to the model before retraining, the total cell throughput has increased by 19.4% when controlled using the retrained model.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"11 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":"87524124","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: 11

Optimal Geometry of Elliptical Target Localization 椭圆目标定位的最优几何

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682134

Na Zhao, Yunlong Wang, Rico Mendrzik, Yuanpeng Liu, Qing Chang, Yuan Shen

引用次数: 0

AI Based Algorithm-Hardware Separation for IoV Security 基于AI的车联网安全算法-硬件分离

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681992

M. Aman, B. Sikdar

{"title":"AI Based Algorithm-Hardware Separation for IoV Security","authors":"M. Aman, B. Sikdar","doi":"10.1109/GCWkshps52748.2021.9681992","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681992","url":null,"abstract":"The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"100 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":"85846087","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

Optimal Resource Allocation in NOMA-assisted D2D Communication with Imperfect Channel State 非完美信道状态下noma辅助D2D通信的最优资源分配

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681954

Rajesh Gupta, S. Tanwar

{"title":"Optimal Resource Allocation in NOMA-assisted D2D Communication with Imperfect Channel State","authors":"Rajesh Gupta, S. Tanwar","doi":"10.1109/GCWkshps52748.2021.9681954","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681954","url":null,"abstract":"Device-to-device communication (D2D) is a promising technology of fifth-generation (5G) networks to maximize spectral efficiency. It permits direct communication between the mobile devices if they are in proximity by ignoring the base station. Despite improving spectral efficiency, it also improves the system data rate, minimizes the communication latency, and reduces the burden on the base station (for data uplink and downlink). The existing communication majorly focused on orthogonal multiple access (OMA), which serves one user at a time for spectrum sharing. To improve the spectral efficiency, non-orthogonal multiple access (NOMA) schemes can be employed that serve multiple users for spectrum sharing. NOMA drastically improves the spectrum sharing, but the devices can cause more interference that degrades overall the network performance. But, successive interference cancellation (SIC) of NOMA mitigates the interference issues and improves the system’s signal to interference noise ratio (SINR). Further, the system’s sum rate and secrecy capacity can be improved with a matching-based algorithm for optimal power allocation. This improves the overall sum rate, which further improves the system’s secrecy capacity against the number of eavesdroppers. Finally, the performance, i.e., overall sum rate and average secrecy capacity of the proposed system is evaluated by considering varying numbers of cellular users, D2D groups, and eavesdroppers.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"12 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":"86993117","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

Cell On/Off Parameter Optimization for Saving Energy via Reinforcement Learning 基于强化学习节能的细胞开/关参数优化

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682160

Minsuk Choi, Kyungrae Kim, Hongjun Jang, Hyokyung Woo, Joan S. Pujol Roig, Yue Wang, Hunje Yeon, Sunghyun Choi, Seowoo Jang

{"title":"Cell On/Off Parameter Optimization for Saving Energy via Reinforcement Learning","authors":"Minsuk Choi, Kyungrae Kim, Hongjun Jang, Hyokyung Woo, Joan S. Pujol Roig, Yue Wang, Hunje Yeon, Sunghyun Choi, Seowoo Jang","doi":"10.1109/GCWkshps52748.2021.9682160","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682160","url":null,"abstract":"Energy cost accounts for a large portion of expenses when operating a cellular mobile network, and it is expected to increase further to support advanced communication features and more base stations as the network evolves. In this work, we address energy saving by turning off cells while minimizing the impact on the performance of the network. However, the challenge here is to optimally and safely manage a cell on/off operation depending on the states of and demands to the networks as they may vary in a wide range in general. In order to be adaptive to the circumstances that a base station is posed and to requirements from its service provider, reinforcement learning-based approaches are used in this work to train personalized or customized neural policies, which operate the cell on/off algorithm. It is shown with a replicative simulator, which has the capability to reproduce the states and behaviors of real RANs (Radio Access Network) using the real data extracted from them, that our approach achieves maximum gain in energy saving while satisfying given constraints on the performance. Also, we propose a couple of operational modes to balance between the performance of energy saving and the cost for running the solution. Through training and evaluation on simple yet demonstrative scenarios, we demonstrate that our approach provides customized solutions and propose various operational options that a service provider can choose from.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"86 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":"84038340","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

Coverage Analysis of Blockchain-enabled Wireless IoMT Networks 基于区块链的无线IoMT网络覆盖分析

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682144

Xuran Li, Hongning Dai, Jie Tian, Dehuan Wan, Dengwang Li

引用次数: 0

Digital Twin-empowered Network Slicing in B5G Networks: Experience-driven approach B5G网络中基于数字孪生的网络切片:体验驱动的方法

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682073

F. Naeem, Georges Kaddoum, M. Tariq

引用次数: 8

3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks 基于深度学习的3D波束成形在5G及以后无线网络中的安全通信

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681960

Helin Yang, Kwok-Yan Lam, Jiangtian Nie, Jun Zhao, S. Garg, Liang Xiao, Zehui Xiong, M. Guizani

{"title":"3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks","authors":"Helin Yang, Kwok-Yan Lam, Jiangtian Nie, Jun Zhao, S. Garg, Liang Xiao, Zehui Xiong, M. Guizani","doi":"10.1109/GCWkshps52748.2021.9681960","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681960","url":null,"abstract":"Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"80 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":"80026300","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