电力线载波(Power Line Carrier,PLC)通信与无线通信都是配电通信网中的重要通信手段,PLC受线路负载和干扰影响,无线通信易受地域、气候环境影响,目前这2种通信方式均未能形成有机整体。为此,文章研究了融合电力线与无线通信技术的异构...电力线载波(Power Line Carrier,PLC)通信与无线通信都是配电通信网中的重要通信手段,PLC受线路负载和干扰影响,无线通信易受地域、气候环境影响,目前这2种通信方式均未能形成有机整体。为此,文章研究了融合电力线与无线通信技术的异构网络,提出了PLC与无线通信中的物理层频谱检测、信道均衡优化方案,探索了独立MAC层与统一MAC层的融合通信方案,并设计了在不同应用场景下的组网方案,可以有效提升通信整体性能,为智能配电网提供低成本、可靠、灵活接入的信息传输手段。展开更多
Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent...Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.展开更多
The properties of broadcast nature, high densities of deployment and severe resource limitations of sensor and mobile networks make wireless networks more vulnerable to various attacks, including modification of messa...The properties of broadcast nature, high densities of deployment and severe resource limitations of sensor and mobile networks make wireless networks more vulnerable to various attacks, including modification of messages, eavesdropping, network intrusion and malicious forwarding. Conventional cryptography-based security may consume significant overhead because of low-power devices, so current research shifts to the wireless physical layer for security enhancement. This paper is mainly focused on security issues and solutions for wireless communications at the physical layer. It first describes the RSSI-based and channel based wireless authentication methods respectively, and presents an overview of various secrecy capacity analyses of fading channel, MIMO channel and cooperative transmission, and then examines different misbehavior detection methods. Finally it draws conclusions and introduces the direction of our future work.展开更多
文摘电力线载波(Power Line Carrier,PLC)通信与无线通信都是配电通信网中的重要通信手段,PLC受线路负载和干扰影响,无线通信易受地域、气候环境影响,目前这2种通信方式均未能形成有机整体。为此,文章研究了融合电力线与无线通信技术的异构网络,提出了PLC与无线通信中的物理层频谱检测、信道均衡优化方案,探索了独立MAC层与统一MAC层的融合通信方案,并设计了在不同应用场景下的组网方案,可以有效提升通信整体性能,为智能配电网提供低成本、可靠、灵活接入的信息传输手段。
基金supported by National Natural Science Foundation of China(62101088,61801076,61971336)Natural Science Foundation of Liaoning Province(2022-MS-157,2023-MS-108)+1 种基金Key Laboratory of Big Data Intelligent Computing Funds for Chongqing University of Posts and Telecommunications(BDIC-2023-A-003)Fundamental Research Funds for the Central Universities(3132022230).
文摘Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.
基金supported in part by State Key Program of National Nature Science Foundation of China under Grant No.60932003National High Technical Research and Development Program of China (863 Program ) under Grant No.2007AA01Z452
文摘The properties of broadcast nature, high densities of deployment and severe resource limitations of sensor and mobile networks make wireless networks more vulnerable to various attacks, including modification of messages, eavesdropping, network intrusion and malicious forwarding. Conventional cryptography-based security may consume significant overhead because of low-power devices, so current research shifts to the wireless physical layer for security enhancement. This paper is mainly focused on security issues and solutions for wireless communications at the physical layer. It first describes the RSSI-based and channel based wireless authentication methods respectively, and presents an overview of various secrecy capacity analyses of fading channel, MIMO channel and cooperative transmission, and then examines different misbehavior detection methods. Finally it draws conclusions and introduces the direction of our future work.
