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Communication Systems and Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 1382

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: machine learning; deep neural networks

Special Issue Information

Dear Colleagues,

In today's interconnected world, communication systems and networks form the backbone of modern society, facilitating the seamless exchange of information across vast distances and diverse platforms. The evolution of these systems has been propelled by rapid advancements in technology, ranging from wireless communications, machine learning, and AI applications in network management devices to satellite networks and beyond. This Special Issue on "Communication Systems and Networks" seeks to explore the latest innovations and emerging trends in this critical domain.

Communication systems and networks are pivotal in enabling global connectivity, supporting everything from personal communications to industrial automation and smart city infrastructure. Recent years have witnessed remarkable progress in areas such as 5G deployment, artificial-intelligence-driven network optimization, cybersecurity strategies, and the integration of renewable energy sources into communication infrastructure. Topics of interest include, but are not limited to, the following:

  • Advanced wireless communication technologies;
  • Security and privacy in communication networks;
  • Link prediction and scheduling in networks;
  • Machine learning and AI applications in network management;
  • Applications of communication networks in smart cities, smart power, and beyond.

Dr. Jian Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • communication systems
  • communication networks
  • machine learning
  • applications of communication networks

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Published Papers (1 paper)

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Research

21 pages, 4338 KiB  
Article
Multi-Head Structural Attention-Based Vision Transformer with Sequential Views for 3D Object Recognition
by Jianjun Bao, Ke Luo, Qiqi Kou, Liang He and Guo Zhao
Appl. Sci. 2025, 15(6), 3230; https://doi.org/10.3390/app15063230 - 16 Mar 2025
Viewed by 993
Abstract
Multi-view image classification tasks require the effective extraction of both spatial and temporal features to fully leverage the complementary information across views. In this study, we propose a lightweight yet powerful model, Multi-head Sparse Structural Attention-based Vision Transformer (MSSAViT), which integrates Structural Self-Attention [...] Read more.
Multi-view image classification tasks require the effective extraction of both spatial and temporal features to fully leverage the complementary information across views. In this study, we propose a lightweight yet powerful model, Multi-head Sparse Structural Attention-based Vision Transformer (MSSAViT), which integrates Structural Self-Attention mechanisms into a compact framework optimized for multi-view inputs. The model employs a fixed MobileNetV3 as a Feature Extraction Module (FEM) to ensure consistent feature patterns across views, followed by Spatial Sparse Self-Attention (SSSA) and Temporal Sparse Self-Attention (TSSA) modules that capture long-range spatial dependencies and inter-view temporal dynamics, respectively. By leveraging these structural attention mechanisms, the model achieves the effective fusion of spatial and temporal information. Importantly, the total model size is reduced to 6.1 M with only 1.5 M trainable parameters, making it highly efficient. Comprehensive experiments demonstrate the proposed model’s superior performance and robustness in multi-view classification tasks, outperforming baseline methods while maintaining a lightweight design. These results highlight the potential of MSSAViT as a practical solution for real-world applications under resource constraints. Full article
(This article belongs to the Special Issue Communication Systems and Networks)
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