GLOBECOM 2022 - 2022 IEEE Global Communications Conference最新文献_第9页

Learning to Predict and Optimize Imperfect MIMO System Performance: Framework and Application 学习预测和优化不完美MIMO系统性能:框架和应用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001369

Jingyi Su, Fanlin Meng, Shengheng Liu, Yongming Huang, Zhaohua Lu

引用次数: 1

Machine Learning-based Flexible Payload Power Resource Allocation for Non-orthogonal SATCOM 基于机器学习的非正交卫星通信灵活载荷功率资源分配

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000745

Yazhou Zhu, C. Hofmann, A. Knopp

引用次数: 0

Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System 基于深度学习的irs辅助ISAC系统信道估计

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001672

Yu Liu, Ibrahim Al-Nahhal, O. Dobre, Fanggang Wang

引用次数: 2

Flowing the Information from Shannon to Fisher: Towards the Fundamental Tradeoff in ISAC 信息从香农流向费雪:关于ISAC的基本权衡

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001144

Yifeng Xiong, Fan Liu, Yuanhao Cui, W. Yuan, T. Han

引用次数: 17

Optimization of Image Transmission in Semantic Communication Networks 语义通信网络中图像传输的优化

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000686

Wenjing Zhang, Yining Wang, Mingzhe Chen, Tao Luo, D. Niyato

{"title":"Optimization of Image Transmission in Semantic Communication Networks","authors":"Wenjing Zhang, Yining Wang, Mingzhe Chen, Tao Luo, D. Niyato","doi":"10.1109/GLOBECOM48099.2022.10000686","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10000686","url":null,"abstract":"In this paper, a semantic communication framework for image transmission is investigated. In the framework, a server transmits image data to a set of users utilizing semantic communication techniques, which enable the server to transmit only the semantic information that accurately captures the meaning of an image. To evaluate the performance of the studied semantic communication system, we propose a multimodal metric called image-to-graph semantic similarity (ISS). The significance of this new metric is that it can measure the correlation of the meaning between semantic information and the original image. To meet the ISS requirement of each user, the server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem whose goal is to minimize the average transmission latency while reaching the ISS requirement. To solve this problem, we propose a model-based actor critic deep reinforcement learning (DRL) algorithm. Compared to traditional actor critic DRL, in the proposed algorithm, we design a novel value function to improve the action exploration thus improving the probability of finding an optimal solution. Simulation results show that the proposed method can reduce the transmission delay by 16.4% and improves the convergence speed by up to 50% compared to the traditional actor critic DRL.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"127 11 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":"124238185","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

Artificial Noise Assisted Interference Alignment for Physical Layer Security Enhancement 增强物理层安全性的人工噪声辅助干涉对准

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000712

Lin Hu, Junxiang Peng, Yan Zhang, H. Wen, Shuai Tan, Jiabing Fan

{"title":"Artificial Noise Assisted Interference Alignment for Physical Layer Security Enhancement","authors":"Lin Hu, Junxiang Peng, Yan Zhang, H. Wen, Shuai Tan, Jiabing Fan","doi":"10.1109/GLOBECOM48099.2022.10000712","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10000712","url":null,"abstract":"Secure transfer of wireless information is becoming a critical issue in multi-user interference networks. In this paper, we consider secure transmission from a source (Alice) to a legitimate destination (Bob), coexisting with a passive eaves-dropper (Eve) and $K$ transceiver pairs. By assuming that only statistical channel state information (CSI) of Eve and local CSIs of legitimate users are known, a secrecy beamforming scheme with artificial noise (AN) is designed for secure transmission, and a modified interference alignment (IA) scheme is proposed for secrecy enhancement. Unlike the conventional AN-aided IA approaches which may lead to private signal cancellation, we propose a novel design modification with security protection. Moreover, a definite connection between improperness and in-feasibility of IA is established, to provide guiding insights on IA requirements. Based on a strict mathematical analysis, we further characterize the impact of transmit power on transceiver design and secrecy performance. Numerical results confirm that our design enables high transmission security with performance guarantee, and thus is suitable and stable for physical layer security (PLS) in multi-user interference networks.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"93 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":"124255336","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

Adaptive Quantization based on Ensemble Distillation to Support FL enabled Edge Intelligence 基于集成蒸馏的自适应量化支持FL启用边缘智能

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001182

Yijing Liu, Shuang Qin, Gang Feng, D. Niyato, Yao Sun, Jianhong Zhou

{"title":"Adaptive Quantization based on Ensemble Distillation to Support FL enabled Edge Intelligence","authors":"Yijing Liu, Shuang Qin, Gang Feng, D. Niyato, Yao Sun, Jianhong Zhou","doi":"10.1109/GLOBECOM48099.2022.10001182","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10001182","url":null,"abstract":"Federated learning (FL) has recently become one of the most acknowledged technologies in promoting the development of intelligent edge networks with the ever-increasing computing capability of user equipment (UE). In traditional FL paradigm, local models are usually required to be homogeneous for aggregation to achieve an accurate global model. Moreover, considerable communication cost and training time may be incurred in resource-constrained edge networks due to a large number of UEs participating in model transmission and the large size of transmitted models. Therefore, it is imperative to develop effective training schemes for heterogeneous FL models, while reducing communication cost as well as training time. In this paper, we propose an adaptive quantization scheme based on ensemble distillation (AQeD) for FL to facilitate personalized quantized model training over heterogeneous local models with different size, structure, and quantization level, etc. Specifically, we design an augmented loss function by jointly considering distillation loss function, quantization values and available wireless resources, where UEs train their local personalized machine learning models and send the quantized models to a server. Based on local quantized models, the server first performs global aggregation for cluster ensembles and then sends the aggregated model of the cluster back to the participating UEs. Numerical results show that our proposed AQeD scheme can significantly reduce communication cost as well as training time in comparison with some known state-of-the-art solutions.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"2 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":"114826596","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

Volunteer Selection in Collaborative Crowdsourcing with Adaptive Common Working Time Slots 基于自适应公共工作时段的协同众包志愿者选择

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001191

Riya Samanta, Vaibhav Saxena, S. Ghosh, Sajal K. Das

引用次数: 1

A Novel Maximum Distance Separable Code Based RIS-OFDM: Design and Optimization 基于最大距离可分离码的新型RIS-OFDM:设计与优化

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10000644

Yiqian Huang, Ping Yang, Yue Xiao, Ming Xiao, Shaoqian Li, Wei Xiang

引用次数: 0

Misbehavior Detection in Vehicular Networks: An Ensemble Learning Approach 车辆网络中的不当行为检测:一种集成学习方法

GLOBECOM 2022 - 2022 IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/GLOBECOM48099.2022.10001264

R. Sedar, Charalampos Kalalas, Paolo Dini, J. Alonso-Zarate, F. V. Gallego

{"title":"Misbehavior Detection in Vehicular Networks: An Ensemble Learning Approach","authors":"R. Sedar, Charalampos Kalalas, Paolo Dini, J. Alonso-Zarate, F. V. Gallego","doi":"10.1109/GLOBECOM48099.2022.10001264","DOIUrl":"https://doi.org/10.1109/GLOBECOM48099.2022.10001264","url":null,"abstract":"Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.","PeriodicalId":313199,"journal":{"name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","volume":"50 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":"114704498","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