Graph-Based Wireless Networking and Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 2983

Special Issue Editors


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Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: wireless communication; semantic communications; wireless sensor networks; the internet of vehicles; machine learning

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: semantic communications; virtual reality; wireless networks; reinforcement learning; machine learning

E-Mail Website
Guest Editor
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Interests: AI/ML enabled wireless communications; compressive sensing; Bayesian inference; multidimensional signal processing

Special Issue Information

Dear Colleagues,

Graph-based theories and algorithms have been applied in many wireless applications, such as internet of things, wireless sensor networks, space–air–ground integrated networks, transport networks, and industrial networks. Graph-based algorithms, such as graph neural networks (GNNs), are suitable for problems in wireless networks because of their strong ability to capture and generalize the spatial and topological information hidden in the network. Hence, graph-based wireless networking and signal processing are expected to achieve state-of-the-art performance.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations in various graph-based networking technologies for advanced applications. We invite researchers to contribute original articles as well as sophisticated review articles. Topics include, but are not limited to, the following areas:

  • Graph-based network coding;
  • Graph-based wireless network optimization;
  • Graph-based theories and algorithms in signal processing;
  • Graph-based semantic encoding and decoding;
  • Graph-based intelligent algorithms;
  • Graph-based wireless data mining;
  • Graph-based protocol design;
  • Wireless sensor networks;
  • Network security;
  • Intelligent application of wireless networks.

We look forward to receiving your contributions.

Technical Committee Member:

  1. Dr. Mingzhe Chen, University of Miami
  2. Dr. Zhaohui Yang, Zhejiang University
  3. Dr. Pengyong Li, Xidian University
  4. Dr. Weijun Cheng, Minzu University of China
  5. Mr. Hanchen Wang, Beijing University of Posts and Telecommunications

Prof. Dr. Tao Luo
Dr. Yining Wang
Prof. Dr. Wei Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • graph-based network coding
  • graph-based wireless network optimization
  • graph signal processing
  • graph-based wireless data mining
  • wireless sensor networks
  • network security

Published Papers (2 papers)

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Research

16 pages, 3641 KiB  
Article
Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data
by Yijie Ma, Ziwei Liu, Nan Yang, Huajian Xu and Gengxin Zhang
Electronics 2024, 13(4), 672; https://doi.org/10.3390/electronics13040672 - 6 Feb 2024
Viewed by 742
Abstract
The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which [...] Read more.
The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen. Full article
(This article belongs to the Special Issue Graph-Based Wireless Networking and Signal Processing)
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18 pages, 2876 KiB  
Article
Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
by Yuan Zhu, Lei Sun, Jianquan Wang, Rong Huang and Xueqin Jia
Electronics 2023, 12(12), 2686; https://doi.org/10.3390/electronics12122686 - 15 Jun 2023
Viewed by 1553
Abstract
5th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new technologies for future industrial networks; 5G-TSN collaboration transmission has drawn more attention because it can provide a guarantee of low-latency, ultra-reliable and deterministic transmission for time-critical automation applications. However, the methodologies of [...] Read more.
5th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new technologies for future industrial networks; 5G-TSN collaboration transmission has drawn more attention because it can provide a guarantee of low-latency, ultra-reliable and deterministic transmission for time-critical automation applications. However, the methodologies of resource scheduling mechanisms in 5G and Time-Sensitive Networking (TSN) are quite different, which may lead to an inefficient Quality of Service (QoS) guarantee for deterministic transmission across 5G and TSN. Therefore, an efficient 5G-TSN joint scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed and analyzed in this article. The proposed algorithm takes both 5G radio channel information and the Gate Control List (GCL) state in the TSN domain into consideration, aiming to provide a latency guarantee for time-triggered applications across 5G and TSN as well as a throughput guarantee for video applications in 5G systems. The simulation results compare the latency and throughput performance of the proposed joint scheduling algorithm with several traditional 5G scheduling algorithms; meanwhile, several GCL setting methods are given to verify the impacts on latency and throughput performance within the proposed algorithm. The simulation results demonstrate that the proposed DDPG-based joint scheduling algorithm can significantly enhance the multi-application-carrying capability of 5G-TSN collaboration architecture. Full article
(This article belongs to the Special Issue Graph-Based Wireless Networking and Signal Processing)
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