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IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/mwc.2023.10183818

引用次数: 3

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IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/mwc.2023.10183817

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Deep Reinforcement Learning Based Task-Oriented Communication in Multi-Agent Systems 基于深度强化学习的多Agent系统中面向任务的通信

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/MWC.003.2200469

Guojun He, Mingjie Feng, Yu Zhang, Guanghua Liu, Yueyue Dai, Tao Jiang

{"title":"Deep Reinforcement Learning Based Task-Oriented Communication in Multi-Agent Systems","authors":"Guojun He, Mingjie Feng, Yu Zhang, Guanghua Liu, Yueyue Dai, Tao Jiang","doi":"10.1109/MWC.003.2200469","DOIUrl":"https://doi.org/10.1109/MWC.003.2200469","url":null,"abstract":"Driven by the increasing demand for executing intelligent tasks in various fields, multi-agent system (MAS) has drawn significant attention recently. An MAS relies on efficient communication between agents to exchange task-relevant information, so as support cooperative operation. Meanwhile, traditional communication systems are bit-oriented, which neglect the content and task relevance of the transmitted data. Thus, if bit-oriented communication patterns are applied in a MAS, a significant amount of task-irrelevant data would be transmitted, leading to communication resource waste and low operational efficiency. Considering that many emerging MASs are data-intensive and delay-sensitive, traditional ways of communication are unfit for these MASs. Task-oriented communication is a promising solution to deal with this issue, but its application in MAS still faces various challenges. In this article, we propose a task-oriented communication based framework for MAS, aiming to support efficient cooperation among agents. This framework specifies the collection, transmission, and processing of task-relevant information, in which task relevance is fully utilized to enhance communication efficiency. Based on the proposed framework, we then apply deep reinforcement learning (DRL) to implement task-oriented communication, in which a modular design and an end-to-end design for information extraction, data transmission, and task execution are proposed. Finally, the open problems for future research are discussed.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"112-119"},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46018285","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

From Digital Twin to Metaverse: The Role of 6G Ultra-Reliable and Low-Latency Communications with Multi-Tier Computing 从数字孪生到元宇宙：6G超可靠低延迟通信在多层计算中的作用

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/MWC.014.2200371

T. Duong, Dang Van-Huynh, Saeed R. Khosravirad, Vishal Sharma, O. Dobre, Hyundong Shin

引用次数: 6

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IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/mwc.2023.10183823

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IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/mwc.2023.10183811

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Toward 6G $text{TK}mu$ Extreme Connectivity: Architecture, Key Technologies and Experiments 走向6G$text｛TK｝mu$极限连接性：体系结构、关键技术和实验

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/MWC.004.2200482

Jianjie You, Dong Liu, Feng-Ming Yang, Jianjun Wu, Jian Lu, Xiaowu Chen, Wenguang Chen, Wen Gao

{"title":"Toward 6G $text{TK}mu$ Extreme Connectivity: Architecture, Key Technologies and Experiments","authors":"Jianjie You, Dong Liu, Feng-Ming Yang, Jianjun Wu, Jian Lu, Xiaowu Chen, Wenguang Chen, Wen Gao","doi":"10.1109/MWC.004.2200482","DOIUrl":"https://doi.org/10.1109/MWC.004.2200482","url":null,"abstract":"Sixth-generation (6G) networks are evolving toward new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $mumathrm{s}$, -scale latency. To this end, an original three-layer 6G network architecture is designed to realize uniform full-spectrum cell-free radio access and provide task-centric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN), which integrates connectivity, computing, Al, data, etc. On this basis, a technological framework of pervasive multi-level (PML) artificial intelligence (Al) is established in the centralized unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G $text{TK}mu$ extreme connectivity. A few most promising key technologies, that is, cell-free massive MIMO, photonics-assisted Terahertz wireless access, and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"86-95"},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44414841","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}

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IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/mwc.2023.10183815

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AI-Driven Proactive Content Caching for 6G AI驱动的6G主动式内容缓存

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/MWC.021.2200535

Guangquan Cheng, Chi Jiang, B. Yue, Ranran Wang, B. Alzahrani, Yin Zhang

{"title":"AI-Driven Proactive Content Caching for 6G","authors":"Guangquan Cheng, Chi Jiang, B. Yue, Ranran Wang, B. Alzahrani, Yin Zhang","doi":"10.1109/MWC.021.2200535","DOIUrl":"https://doi.org/10.1109/MWC.021.2200535","url":null,"abstract":"To address the limitations of the current proactive content caching technology for the 6th generation (6G) mobile network, this article comprehensively analyzes the complex application scenarios of proactive content caching technology for wireless edge networks. It constructs an accurate content popularity prediction model, develops a user-device-oriented proactive content caching mechanism, establishes an interpretable cached content replacement strategy, and designs a reliable interdevice content sharing service model to achieve accurate, effective, trustworthy, and practical results. In this article, we analyze the proactive content caching technology for wireless edge networks. Based on the analysis of the core theory and application scenarios of proactive content caching in wireless edge networks, this article focuses on improving the hit rate of content caching in edge devices, improving the quality-of-experience (QoE) of end-users accessing content, enhancing the robustness of proactive content caching schemes, and conducting in-depth research on the key technologies and methods involved. The proposed proactive content caching technology for wireless edge networks is validated and improved through experimental research.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"180-188"},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43065008","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

Implementing Graph Neural Networks Over Wireless Networks via Over-the-Air Computing: A Joint Communication and Computation Framework 通过空中计算在无线网络上实现图神经网络：一个联合通信和计算框架

IF 12.9 1区计算机科学

IEEE Wireless Communications Pub Date : 2023-06-01 DOI: 10.1109/MWC.012.2200552

Yuzhi Yang, Zhaoyang Zhang, Yuqing Tian, Richeng Jin, Chongwen Huang

{"title":"Implementing Graph Neural Networks Over Wireless Networks via Over-the-Air Computing: A Joint Communication and Computation Framework","authors":"Yuzhi Yang, Zhaoyang Zhang, Yuqing Tian, Richeng Jin, Chongwen Huang","doi":"10.1109/MWC.012.2200552","DOIUrl":"https://doi.org/10.1109/MWC.012.2200552","url":null,"abstract":"A Graph Neural Network (GNN) conducts the graph convolution for structured data and obtains the weighted sum over the vertices according to its graph structure. However, in the context of a wireless network, the traditional separate implementation of a GNN usually requires the full channel state information, which is hard to obtain in practice, especially for the underlying interference channels. On the other hand, Over-the-Air Computing (OAC) is an efficient analog wireless technique in which the weighted sum can be simultaneously calculated over an equivalent wireless superposition channel. Since the main goal of a distributed learning-based system is the fulfillment of the overall learning task instead of its communication rate, OAC is of great potential for implementing such a system. In this article, we exploit some specific features of the wireless interference graphs and propose a novel task-ori-ented OAC-based framework to deploy GNNs more efficiently in wireless networks. In particu-lar, we take advantage of the structural similarity between OAC and the graph convolution oper-ation over an interference graph, and the chan-nel prediction procedure can be merged into the weight updating procedure. Moreover, the inher-ent noise tolerance of a neural network further ensures its convergence and performance. We also conduct case studies based on the proposed framework and discuss the comprehensive future research directions and open problems.","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"30 1","pages":"62-69"},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47403255","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