IEEE Wireless Communications最新文献_第4页

Quantum Machine Intelligence for 6G URLLC 6G URLLC的量子机器智能

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.003.2200382

Fakhar Zaman, Ahmad Farooq, M. A. Ullah, Haejoon Jung, Hyundong Shin, M. Win

{"title":"Quantum Machine Intelligence for 6G URLLC","authors":"Fakhar Zaman, Ahmad Farooq, M. A. Ullah, Haejoon Jung, Hyundong Shin, M. Win","doi":"10.1109/MWC.003.2200382","DOIUrl":"https://doi.org/10.1109/MWC.003.2200382","url":null,"abstract":"Immersive and mission-critical data-driven applications, such as virtual or augmented reality, tactile Internet, industrial automation, and autonomous mobility, are creating unprecedented challenges for ultra-reliable and low-latency communication (URLLC) in the sixth generation (6G) networks. Machine intelligence approaches deep learning, reinforcement learning, and federated learning (FL), to provide new paradigms to ensure 6G URLLC on the stream of big data training. However, classical limitations of machine learning capabilities make it challenging to achieve stringent 6G URLLC requirements. In this article, we investigate the potential of variational quantum computing and quantum machine learning (QML) for 6G URLLC by utilizing the advantage of quantum resources, such as superposition, entanglement, and quantum parallelism. The underlying idea is to integrate quantum machine intelligence with 6G networks to ensure stringent 6G URLLC requirements. As an example, we demonstrate the quantum approximate optimization algorithm for NP-hard URLLC task offloading optimization problems. The variational quantum computation for QML is also adopted in wireless networks to enhance the learning rate of machine intelligence and ensure the learning optimality for mission-critical applications. Considering the security and privacy issues, as well as computational-resource overheads in FL, distributed quantum computation in blind and remote fashions is further investigated for quantum-assisted FL.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"22-30"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43630785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

5G MEC-Based Intelligent Computation Offloading in Power Robotic Inspection 基于5G mec的电力机器人巡检智能计算卸载

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.003.2200350

Wei Wang, Rui Qu, Haijun Liao, Zhao Wang, Zhenyu Zhou, Zhongyuan Wang, S. Mumtaz, M. Guizani

{"title":"5G MEC-Based Intelligent Computation Offloading in Power Robotic Inspection","authors":"Wei Wang, Rui Qu, Haijun Liao, Zhao Wang, Zhenyu Zhou, Zhongyuan Wang, S. Mumtaz, M. Guizani","doi":"10.1109/MWC.003.2200350","DOIUrl":"https://doi.org/10.1109/MWC.003.2200350","url":null,"abstract":"Power robotic inspection plays a critical role in the realization of real-time visualization and perception of substation in power grid. 5G mobile edge computing (MEC) has emerged as a promising solution to provide the large bandwidth, wide connectivity, and proximate computing capabilities for the computation offloading of power robotic inspection with stringent delay requirements. This article proposes a 5G MEC-based intelligent computation offloading framework in power robotic inspection to cope with multi-dimension entity heterogeneity, environment dynamics, and inspection delay guarantee. Specifically, the proposed framework and the implementation procedures of computation offloading are firstly elaborated, and the research challenges are outlined. Then, we propose an artificial intelligence (AI)-enabled multi-dimension collaborative optimization algorithm of route planning and task offloading to address the low-latency computation offloading problem under queue stability constraint. A case study is provided to verify the superiority of delay and queue backlog performance through simulation results.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"66-74"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47064655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On Multi-Robot Data Collection and Offloading for Space-Aerial-Surface Computing 空间航空表面计算中的多机器人数据采集与卸载

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.005.2200400

Haoran Mei, Limei Peng

引用次数: 1

Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems 用于隐私保护和弹性移动机器人系统的去中心化P2P联合学习

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.004.2200381

Xiaokang Zhou, Wei Liang, K. Wang, Zheng Yan, L. Yang, Wei Wei, Jianhua Ma, Qun Jin

{"title":"Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems","authors":"Xiaokang Zhou, Wei Liang, K. Wang, Zheng Yan, L. Yang, Wei Wei, Jianhua Ma, Qun Jin","doi":"10.1109/MWC.004.2200381","DOIUrl":"https://doi.org/10.1109/MWC.004.2200381","url":null,"abstract":"Swarms of mobile robots are being widely applied for complex tasks in various practical scenarios toward modern smart industry. Federated learning (FL) has been developed as a promising privacy-preserving paradigm to tackle distributed machine learning tasks for mobile robotic systems in 5G and beyond networks. However, unstable wireless network conditions of the complex and harsh working environment may lead to poor communication quality and bring big challenges to traditional centralized global training in FL models. In this article, a Peer-to-Peer (P2P) based Privacy-Perceiving Asynchronous Federated Learning (PPAFL) framework is introduced to realize the decentralized model training for secure and resilient modern mobile robotic systems in 5G and beyond networks. Specifically, a reputation-aware coordination mechanism is designed and addressed to coordinate a group of smart devices dynamically into a virtual cluster, in which the asynchronous model aggregation is conducted in a decentralized P2P manner. A secret sharing based communication mechanism is developed to ensure an encrypted P2P FL process, while a Secure Stochastic Gradient Descent (SSGD) scheme is integrated with an Autoencoder and a Gaussian mechanism is developed to ensure an anonymized local model update, communicating within a few neighboring clients. The case study based experiment and evaluation in three different application scenarios demonstrate that the PPAFL can effectively improve the security and resilience issues compared with the traditional centralized approaches for smart mobile robotic applications in 5G and beyond networks.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"82-89"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47846675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Energy Efficient Computation Offloading in Mobile Edge Computing 移动边缘计算中的节能计算卸载

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.2023.10105148

B. Rong

{"title":"Energy Efficient Computation Offloading in Mobile Edge Computing","authors":"B. Rong","doi":"10.1109/MWC.2023.10105148","DOIUrl":"https://doi.org/10.1109/MWC.2023.10105148","url":null,"abstract":"This book provides a comprehensive introduction to energy efficient computation offloading and resource management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling, and resource scheduling. With the proliferation of mobile devices and the development of Internet of Things (IoT), more and more computation- intensive and delay-sensitive applications are running on terminal devices, which results in high-energy consumption and heavy computation load of devices. Due to the size and hardware constraints, the battery lifetime and computing capacity of terminal devices are limited. Consequently, it is hard to process all of the tasks locally while satisfying Quality of Service (QoS) requirements for devices. Mobile Edge Computing (MEC) is considered a promising paradigm that deploys computing resources at the network edge near terminal devices. With the help of MEC, terminal devices can achieve better computing performance and battery lifetime while ensuring QoS. This book discusses energy efficient computation offloading and resource allocation for MEC deeply. However, the introduction of MEC provokes challenges under energy-constrained and dynamic conditions. Therefore, it is very important to design a strategy for energy efficient computation offloading and resource allocation. To this end, this book discusses issues, such as task offloading, channel allocation, frequency scaling, and resource scheduling in MEC. The presented computation offloading and energy management solutions and the corresponding research results in this book can provide some valuable insights for practical applications of MEC and motivate new ideas for future MEC-enabled IoT networks.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"8-8"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46449854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Space Simultaneous Information and Power Transfer: An Enhanced Technology for Miniaturized Satellite Systems 空间同步信息和能量传输:小型化卫星系统的一种增强技术

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.001.2200002

Gaofeng Pan, Haoxing Zhang, Rui Zhang, Shuai Wang, Jianping An, Mohamed-Slim Alouini

引用次数: 4

Downlink Cell-Free Fixed Wireless Access: Architectures, Physical Realities, and Research Opportunities 下行链路无小区固定无线接入：体系结构、物理现实和研究机会

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.014.2100672

Yangyishi Zhang, Minglei You, G. Zheng, A. A. Rawi, Anvar Tukmanov

{"title":"Downlink Cell-Free Fixed Wireless Access: Architectures, Physical Realities, and Research Opportunities","authors":"Yangyishi Zhang, Minglei You, G. Zheng, A. A. Rawi, Anvar Tukmanov","doi":"10.1109/MWC.014.2100672","DOIUrl":"https://doi.org/10.1109/MWC.014.2100672","url":null,"abstract":"Recently, a new paradigm of wireless access, termed as cell-free massive multiple-input multiple-output (MIMO), has drawn significant research interest. Its primary distinction from conventional massive MIMO aided cellular networks is the ability to eliminate the detrimental inter-cell interference (ICI), or to convert ICI, into extra power for the intended signal via a multi-cell cooperation approach originated from network MIMO. However, the information-theoretical limit of cell-free access is achieved at the expense of large network configuration overhead and high MIMO processing complexity. Because of the dynamic nature of wireless channels, the global channel state information (CSI) invoked for network MIMO quickly becomes outdated, leading to performance degradation. This article focuses on the cell-free implementation of fixed wireless access (FWA), a complementary solution to fiber-to-the-premise (FTTP) where the latter is prohibitively expensive. In particular, we discuss the centralization architectures and channel characteristics of cell-free FWA, as well as their joint implications on imperfect CSI performance. Moreover, measurement-based offline simulations show that the long coherence time (‘quasi-static’) assumption of real-world FWA channels is only valid against a completely motionless background, and, thus, it should not be used in FWA system design or performance analysis. Finally, we present new research opportunities for cell-free FWA in terms of physical infrastructure, data processing, and machine learning.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"155-162"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45610898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Robot Distributed Communication in Heterogeneous Robotic Systems on 5G Networking 5G网络下异构机器人系统中的多机器人分布式通信

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.001.2200315

Zhihan Lv, Chen-Chia Cheng, Haibin Lv

引用次数: 3

Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions 联合学习中的后门攻击和防御：现状、分类和未来方向

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.017.2100714

Xueluan Gong, Yanjiao Chen, Qian Wang, Weihan Kong

{"title":"Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions","authors":"Xueluan Gong, Yanjiao Chen, Qian Wang, Weihan Kong","doi":"10.1109/MWC.017.2100714","DOIUrl":"https://doi.org/10.1109/MWC.017.2100714","url":null,"abstract":"The federated learning framework is designed for massively distributed training of deep learning models among thousands of participants without compromising the privacy of their training datasets. The training dataset across participants usually has heterogeneous data distributions. Besides, the central server aggregates the updates provided by different parties, but has no visibility into how such updates are created. The inherent characteristics of federated learning may incur a severe security concern. The malicious participants can upload poisoned updates to introduce backdoored functionality into the global model, in which the backdoored global model will misclassify all the malicious images (i.e., attached with the backdoor trigger) into a false label but will behave normally in the absence of the backdoor trigger. In this work, we present a comprehensive review of the state-of-the-art backdoor attacks and defenses in federated learning. We classify the existing backdoor attacks into two categories: data poisoning attacks and model poisoning attacks, and divide the defenses into anomaly updates detection, robust federated training, and backdoored model restoration. We give a detailed comparison of both attacks and defenses through experiments. Lastly, we pinpoint a variety of potential future directions of both backdoor attacks and defenses in the framework of federated learning.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"114-121"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41396191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services 6G增强型超可靠和低延迟服务的机器学习

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-04-01 DOI: 10.1109/MWC.006.2200407

Yan Liu, Yansha Deng, A. Nallanathan, Jinhong Yuan

{"title":"Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services","authors":"Yan Liu, Yansha Deng, A. Nallanathan, Jinhong Yuan","doi":"10.1109/MWC.006.2200407","DOIUrl":"https://doi.org/10.1109/MWC.006.2200407","url":null,"abstract":"Ultra-reliable and low-latency communications (URLLC), as one of the major communication services of the fifth-generation (5G) and the sixth-generation (6G) cellular networks, is critical to supporting a variety of emerging mission-critical applications. However, the modern mobile networks could not satisfy the latency and reliability requirements, as well as other Quality of Service (QoS) requirements, including spectrum efficiency, energy efficiency, capacity, jitter, round-trip delay, network coverage, etc. To fulfill diverse QoS requirements for various URLLC applications, machine learning (ML) solutions are promising for future 6G networks. In this article, we first categorize the 6G URLLC vision into three connectivity characteristics, including ubiquitous connectivity, deep connectivity, and holographic connectivity, with their corresponding unique QoS requirements. We then identify potential challenges in meeting these connectivity requirements, and investigate promising ML solutions to achieve the intelligent connectivity for the 6G URLLC service. We further discuss how to implement the ML algorithms to guarantee the QoS requirements for different URLLC scenarios, including mobility URLLC, massive URLLC, and broadband URLLC. Finally, we present a case study of downlink URLLC channel access problems, solved by centralized deep reinforcement learning (CDRL) and federated DRL (FDRL), respectively, which validates the effectiveness of machine learning for URLLC services.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"48-54"},"PeriodicalIF":12.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48427598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9