GLOBECOM 2022 - 2022 IEEE Global Communications Conference最新文献_第10页

Caching and Recommendation Decisions at Transcoding-Enabled Base Stations 支持转码的基站的缓存和推荐决策

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000796

Dimitra Tsigkari, T. Spyropoulos

{"title":"Caching and Recommendation Decisions at Transcoding-Enabled Base Stations","authors":"Dimitra Tsigkari, T. Spyropoulos","doi":"10.1109/GLOBECOM48099.2022.10000796","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10000796","url":null,"abstract":"In the context of on-demand video streaming services, both the caching and the recommendation decisions have an impact on the user satisfaction, and thus, financial implications for the Content Provider (CP). The idea of co-designing these decisions has been recently proposed in the literature as a way to minimize delivery costs and traffic at the backbone Internet. However, related work does not take into account that every content exists in multiple versions/streaming qualities, or at best treats each version as a separate content, when it comes to caching. In this paper, we explore how transcoding a content at the edge could avoid placing multiple related versions of this content in the same cache, thus better utilizing capacity (leading to an increase of the CP's profit). To this end, we formulate the problem of jointly deciding on caching, recommendations, and user-transcoder assignments with the goal of increasing the profit (revenue minus the incurred costs). We propose an iterative algorithm that is based on a decomposition of the formulated problem into two subproblems. We show that both subproblems, although NP-hard, are equivalent to problems in the literature for which algorithms with approximation guarantees exist. Our numerical evaluations in realistic scenarios show that the proposed policy leads to important financial gains of up to 29% when compared to the scenario where edge transcoding is not exploited.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705791","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

False Data Injection Attack Against Cyber-Physical Systems Protected by a Watermark 基于水印保护的信息物理系统的虚假数据注入攻击

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001170

Khalil Guibene, N. Messai, Marwane Ayaida, L. Khoukhi, Atika Rivenq, Y. Elhillali

引用次数: 0

Vehicle-Assisted Data Delivery Based on Trajectory Prediction 基于轨迹预测的车辆辅助数据传输

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001329

R. Sousa, A. Boukerche, A. Loureiro

引用次数: 0

Towards Spatial Location Aided Fully-Distributed Dynamic Routing for LEO Satellite Networks 低轨道卫星网络空间定位辅助全分布式动态路由研究

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001698

Guoliang Xu, Yanyun Zhao, Yongyi Ran, Ruili Zhao, Jiangtao Luo

引用次数: 1

Anomaly Traffic Detection with Federated Learning toward Network-based Malware Detection in IoT 基于联邦学习的物联网网络恶意软件异常流量检测

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000633

T. Nishio, Masataka Nakahara, Norihiro Okui, A. Kubota, Yasuaki Kobayashi, K. Sugiyama, R. Shinkuma

引用次数: 1

TWLBR: Multi-Human Through-Wall Localization and Behavior Recognition Based on MIMO Radar 基于MIMO雷达的多人穿墙定位与行为识别

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001129

Dongsheng Zhu, Changlong Wang, Chong Han, Jian Guo, Lijuan Sun

{"title":"TWLBR: Multi-Human Through-Wall Localization and Behavior Recognition Based on MIMO Radar","authors":"Dongsheng Zhu, Changlong Wang, Chong Han, Jian Guo, Lijuan Sun","doi":"10.1109/GLOBECOM48099.2022.10001129","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10001129","url":null,"abstract":"Human localization and behavior recognition (HLBR) is an important research topic in wireless sensing and computer vision. In existing work, most of the approaches using sensors such as cameras and mmWave radar cannot solve the wall occlusion problem, while the approaches using Wi-Fi can penetrate the wall but cannot locate human precisely due to its bandwidth limitation. All these limit the application of HLBR in reality. In this paper, we propose TWLBR, a real-time detection system for inferring the localization and behavior of human behind brick walls from radar heatmaps. In this system, we design a multiple-input multiple-output (MIMO) radar in the frequency range of 1–2 GHz and a multi-feature fusion network based on a 3D convolutional neural network (CNN) and a transformer. The network takes four radar heatmaps with background removal as input and outputs the location and behavior of the target humans. Our experiments show that TWLBR can locate and recognize the behavior of target humans behind a 24 cm brick wall with a localization accuracy of 6.2 cm and a behavior recognition accuracy of 96.37%, which is better than existing methods.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116839952","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

Deep Reinforcement Learning for AoI Aware VNF Placement in Multiple Source Systems 多源系统中AoI感知VNF放置的深度强化学习

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001066

Zhenke Chen, He Li, K. Ota, M. Dong

{"title":"Deep Reinforcement Learning for AoI Aware VNF Placement in Multiple Source Systems","authors":"Zhenke Chen, He Li, K. Ota, M. Dong","doi":"10.1109/GLOBECOM48099.2022.10001066","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10001066","url":null,"abstract":"Age of Information (AoI) is a newly emergent performance metric to quantify the freshness of data from destinations' perspectives. In this paper, we investigate and analyze AoI in the context of a multiple source updating system. In such a system, multiple loT devices continuously monitors physical environment and sends data to a remote destination for status updates through a Network Function Virtualization (NFV)-enabled network. Considering that the Virtual Network Function (VNF) placement can unnecessarily influence the AoI of the updates, we study the VNF placement problem in such a system. The problem is hence formulated as a mathematical optimization problem aiming to minimize the long-term average AoI of all updates received at the destination. To solve this prob-lem, we propose a Deep Reinforcement Learning (DRL)-based VNF placement approach called VNF-AoI, where a learning agent or decision-maker interacts with a system environment and consequently provides an optimal VNF placement policy according to the experience it has learned. Finally, we conduct extensive simulations to validate the effectiveness of our proposed approach. Numerical results clearly demonstrate that our VNF-AoI surpasses other two baseline algorithms by averagely 13.8 % higher acceptance ratio and 20.3 % lower average AoI at the destination.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117088682","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

Non-cooperative Learning for Robust Spectrum Sharing in Connected Vehicles with Malicious Agents 恶意代理网联车辆鲁棒频谱共享的非合作学习

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000791

Haoran Peng, Hanif Rahbari, S. Yang, Li-Chun Wang

{"title":"Non-cooperative Learning for Robust Spectrum Sharing in Connected Vehicles with Malicious Agents","authors":"Haoran Peng, Hanif Rahbari, S. Yang, Li-Chun Wang","doi":"10.1109/GLOBECOM48099.2022.10000791","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10000791","url":null,"abstract":"Multi-agent reinforcement learning (MARL) has pre-viously been employed for efficient spectrum sharing among co-operative connected vehicles. However, we show in this paper that existing MARL models are not robust against non-cooperative or malicious agents (vehicles) whose spectrum selection strategy may cause congestion and reduce the spectrum utilization. For example, a selfish (non-cooperative) agent aims to only maximize its own spectrum utilization, irrespective of the overall system efficiency and spectrum availability to others. We investigate and analyze the MARL-based spectrum sharing problem in connected vehicles including vehicles (agents) with selfish or sabotage strategies. We then develop a theoretical framework to consider the selfish agent, and study various adversarial scenarios (including attacks with disruptive goals) via simulations. Our robust MARL approach where “robust” agents are trained to be prepared for selfish agents in testing phase achieves more resiliency in the presence of a selfish agent and even a sabotage one; achieving 6.7%~20% and 50.7% ~ 138% higher unicast throughput and broadcast delivery success rate over regular benign agents, respectively.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"692 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120881793","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

Packet Delivery Ratio Guarantees for Differentiated LoRaWanServices 差分lorawan业务的包投递率保证

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001145

A. Aimi, F. Guillemin, Stéphane Rovedakis, Stefano Secci

引用次数: 1

Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization 基于原子范数最小化的近场RIS模型单快照定位

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000689

Omar Rinchi, A. Elzanaty, Ahmad Alsharoa

{"title":"Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization","authors":"Omar Rinchi, A. Elzanaty, Ahmad Alsharoa","doi":"10.1109/GLOBECOM48099.2022.10000689","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10000689","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125969347","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: 3