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Telecom
Special Issues
Emerging Technologies in Communications and Machine Learning
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Emerging Technologies in Communications and Machine Learning

A special issue of Telecom (ISSN 2673-4001).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 10593

Editors

Prof. Dr. Sotirios K. Goudos

E-Mail Website
Guest Editor
Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave component design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chrysostomos Stylios

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: fuzzy cognitive maps; artificial intelligence; big data; data mining; soft computing; computational intelligence techniques; biosignal processing and analysis; modeling and decision support systems; support vector machines; knowledge-based systems; simulation and modeling complex systems; intelligent systems; hierarchical systems and supervisory control; intelligent manufacturing systems; open innovation; technology transfer; educational methodologies and tools; virtual and augmentative reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2025) will take place in Patras, Greece, from 19 to 21 September 2025. The SEEDA-CECNSM 2025 technical program includes all aspects of communications and network technologies as well as artificial intelligence, machine learning, internet of things, cloud computing, and cyber-physical systems. This Special Issue extends a warm invitation to submissions not only from conference attendees but also from researchers who are actively involved in or harbor a keen interest in the area of communications and machine learning. Potential topics include, but are not limited to, the following:

  • Internet of things;
  • Cloud computing;
  • AI for communications systems;
  • Wireless communications;
  • Network systems.

Prof. Dr. Sotirios K. Goudos
Prof. Dr. Chrysostomos Stylios
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Telecom 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 1400 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

  • antenna design
  • propagation
  • wireless communications
  • network systems

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Published Papers (2 papers)

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Research

21 pages, 20330 KB  
Article
A Fault Diagnosis Method for Mobile Communication Networks Based on Improved Convolutional Neural Networks
by Hongliang Tian, Bolin Song and Xiaoke Liu
Telecom 2026, 7(3), 60; https://doi.org/10.3390/telecom7030060 - 28 May 2026
Viewed by 277
Abstract
In response to the shortcomings of current mobile communication network (MCN) fault diagnosis methods, such as the insufficient robustness of time-series-spectrum features and the limited ability to capture long-distance dependencies, an improved convolutional neural network is proposed, along with a hybrid diagnosis method [...] Read more.
In response to the shortcomings of current mobile communication network (MCN) fault diagnosis methods, such as the insufficient robustness of time-series-spectrum features and the limited ability to capture long-distance dependencies, an improved convolutional neural network is proposed, along with a hybrid diagnosis method based on time-frequency perception and a lightweight deep network (TL-FDN). The TL-FDN introduces a time-series-spectrum feature enhancement module (TFN-E) at the input end, and enhances the robustness of features through a learnable Gabor filter bank. The main architecture employs a hybrid module that integrates a lightweight convolution (LiConv-Block) and a broadcast self-attention (BSA) mechanism (Former-Block), effectively balancing the efficiency of local feature extraction with the capture of global time-series dependencies. Additionally, the model uses a multi-task loss function to achieve joint diagnosis of fault type and fault location. The experimental results show that the average accuracy of the proposed TL-FDN method is 98.6%, which is 3.5% higher than that of the standard convolutional + standard attention baseline method. To strictly evaluate the performance improvement, this paper conducted a non-parametric Wilcoxon signed-rank test in 10 independent experiments. The p-values of the core model indicators were all strictly less than 0.05. These results statistically confirm the superiority of TL-FDN in the fault type identification and location tasks, while maintaining a lightweight parameter quantity suitable for edge-end deployment. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Machine Learning)
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23 pages, 471 KB  
Article
Harvest-Now, Decrypt-Later: A Temporal Cybersecurity Risk in the Quantum Transition
by Francis Kagai, Philip Branch, Jason But and Rebecca Allen
Telecom 2025, 6(4), 100; https://doi.org/10.3390/telecom6040100 - 18 Dec 2025
Cited by 7 | Viewed by 9148
Abstract
Telecommunication infrastructures rely on cryptographic protocols designed for long-term confidentiality, yet data exchanged today faces future exposure when adversaries acquire quantum or large-scale computational capabilities. This harvest-now, decrypt-later (HNDL) threat transforms persistent communication records into time-dependent vulnerabilities. We model HNDL as a temporal [...] Read more.
Telecommunication infrastructures rely on cryptographic protocols designed for long-term confidentiality, yet data exchanged today faces future exposure when adversaries acquire quantum or large-scale computational capabilities. This harvest-now, decrypt-later (HNDL) threat transforms persistent communication records into time-dependent vulnerabilities. We model HNDL as a temporal cybersecurity risk, formalizing the adversarial process of deferred decryption and quantifying its impact across sectors with varying confidentiality requirements. Our framework evaluates how delayed post-quantum cryptography (PQC) migration amplifies exposure and how hybrid key exchange and forward-secure mechanisms mitigate it. Results show that high-retention sectors such as satellite and health networks face exposure windows extending decades under delayed PQC adoption, while hybrid and forward-secure approaches reduce this risk horizon by over two-thirds. We demonstrate that temporal exposure is a measurable function of data longevity and migration readiness, introducing a network-centric model linking quantum vulnerability to communication performance and governance. Our findings underscore the urgent need for crypto-agile infrastructures that maintain confidentiality as a continuous assurance process throughout the quantum transition. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Machine Learning)
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