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Electronics
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Advances in Networked Systems and Communication Protocols
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Advances in Networked Systems and Communication Protocols

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 711

Special Issue Editors

Dr. Zhixiong Chen

E-Mail Website
Guest Editor
Department of Electronics and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: power system communication; IoT; wireless communication; power line communication
Dr. Danhao Deng

E-Mail Website
Guest Editor
Department of Electronics and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: wireless communication
Dr. Xiaolei Qi

E-Mail Website
Guest Editor
Department of Electronics and Communication Engineering, North China Electric Power University, Baoding 071003, China
Interests: wireless communication

Special Issue Information

Dear Colleagues,

The landscape of wireless communication is undergoing a radical transformation, driven by unprecedented demands for low latency, high reliability, and massive-scale integration. From cloud computing and the Internet of Things (IoT) to smart cities, the emerging applications place extremely high demands on the speed, capacity, and reliability of our networks. However, these advancements are driving the underlying networked systems and communication protocols to their theoretical and practical limits. Existing network architecture and communication protocols, while remarkably resilient, still face fundamental challenges in scalability, security, intelligence, and efficiency when confronted with the demands of next-generation services. This necessitates a paradigm shift toward more intelligent, adaptive, and high-performance networking foundations.

This Special Issue aims to discuss cutting-edge research in the fields of networked systems and communication protocols design, analysis, and implementation. We seek to explore groundbreaking architectures, innovative methods, and practical solutions that address the fundamental challenges of scale, complexity, and dynamism in modern networks. The scope encompasses all facets of modern networking, from theoretical foundations and architectural paradigms to practical deployments and experimental validations. Topics of interest include, but are not limited to, the following:

  • Protocol design for 5G/6G networks;
  • Cloud, edge, and fog computing;
  • AI and machine learning for network management;
  • Network security and privacy;
  • Energy-efficient communication;
  • Power system network design;
  • Integrated space–air–ground networks;
  • Integrated sensing and communication;
  • Internet of Things and ubiquitous networking;
  • Software-defined networking and network virtualization.

Dr. Zhixiong Chen
Dr. Danhao Deng
Dr. Xiaolei Qi
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • next-generation networks
  • communication protocols
  • network architectures
  • wireless communication systems
  • B5G/6G

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Published Papers (1 paper)

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Research

16 pages, 2916 KB  
Article
Deep Learning-Based Relay Selection in a Decode-and-Forward Cooperative System with Energy Harvesting and Signal Space Diversity
by Ahmed Oun, Divyessh Maheshwari and Ahmed Ammar
Electronics 2026, 15(7), 1363; https://doi.org/10.3390/electronics15071363 - 25 Mar 2026
Viewed by 477
Abstract
Deep learning techniques have been widely applied in wireless communication systems to enhance resilience and reduce computational complexity. This paper investigates both traditional and deep learning-based approaches for real-time relay selection in a cooperative communication system with multiple energy-harvesting relays and signal space [...] Read more.
Deep learning techniques have been widely applied in wireless communication systems to enhance resilience and reduce computational complexity. This paper investigates both traditional and deep learning-based approaches for real-time relay selection in a cooperative communication system with multiple energy-harvesting relays and signal space diversity. The assumed relay decoding scheme is decode-and-forward (DF), with selection criteria based on successful decoding from the source, sufficient energy availability, and the best channel to the destination. The system performance is evaluated in terms of outage probability. Monte Carlo simulations are used to determine the exact outage probability of the system and to generate datasets for training machine learning models. The traditional machine learning models implemented include Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machines (SVMs). The deep learning-based method used is the deep neural network (DNN). Two datasets—one with six features and another with nine features—were used for training and testing. The 6-feature datasets are comparatively less random and complex than the 9-feature datasets. The results indicate that among traditional models KNN achieves the highest accuracy and is thus used as a benchmark to compare against DNN performance. For the 9-feature datasets, both KNN and DNN struggle to accurately approximate the exact outage probability, suggesting that the 9-feature datasets are too complex and noisy for effective modeling. However, on the 6-feature datasets, KNN achieves 77% accuracy, while DNN achieves a significantly higher accuracy of 99%. Due to its high accuracy, the DNN model closely approximates the exact outage probability while offering greater computational efficiency compared to the KNN model. These results underscore the potential of deep learning in optimizing real-time relay selection for energy-harvesting cooperative communication systems. Full article
(This article belongs to the Special Issue Advances in Networked Systems and Communication Protocols)
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