5G and Next-Generation Communication Technologies

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 918

Editors


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Guest Editor
Department of Computer Science, Purdue University, West Lafayette, IN, USA
Interests: mobile networking, systems, and security, with a recent focus on renovating 5G access technologies; AI for networks; 5G/IoT security; mobile edge computing (mainly for autonomous drones, vehicles, and robots)

E-Mail Website
Guest Editor
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Interests: wireless networking; mobile networks and systems; network security

Special Issue Information

Dear Colleagues,

We are pleased to invite researchers, professionals, and experts from industry and academia to submit their relevant work to this Special Issue, ‘5G and Next-Generation Communication Technologies’.

This Special Issue aims to take a "Look Back and Look Forward" approach to advance 5G and next-generation wireless communication technologies to meet the ever-increasing demands for higher data throughput, lower latency, massive connectivity, higher reliability, seamless user experiences, and new use cases. Specifically, it aims to bringing together original high-quality contributions that (1) reveal the experience and lessons of 5G networks and wireless communication technologies and (2) explore the challenges and innovations of next-generation wireless communication technologies. We encourage submissions covering, but not limited to, the following topics:

  • 5G and next-generation radio access networks (RANs);
  • 5G and next-generation physical-layer techniques;
  • 5G and next-generation spectrum management and sharing;
  • 5G and next-generation network management and control;
  • Experience and lessons with 5G communication technologies;
  • New AI-empowered techniques for next-generation wireless communication;
  • New use cases for next-generation wireless communication;
  • Standardization and regulation for next-generation wireless communication;
  • Testbeds and tools for next-generation wireless communication;
  • Vision for next-generation wireless communication.

Dr. Chunyi Peng
Prof. Dr. Chiyu Li
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. Network is an international peer-reviewed open access quarterly 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 1200 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

  • 5G
  • 6G
  • next-generation wireless communication
  • radio access network (RAN)
  • radio spectrum
  • network management

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Published Papers (1 paper)

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Research

24 pages, 635 KB  
Article
Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments
by Ahmed Lateef Salih Al-Karawi and Rafet Akdeniz
Network 2026, 6(3), 50; https://doi.org/10.3390/network6030050 - 6 Jul 2026
Viewed by 300
Abstract
Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G [...] Read more.
Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G networks, where edge-native services are required to satisfy stringent latency, bandwidth, and privacy constraints while operating on highly heterogeneous devices and time-varying wireless channels. In practice, however, synchronous FL is often constrained by straggling clients with limited computation capability or unfavorable communication conditions, which increases round latency and reduces overall resource efficiency. To address this challenge, this study develops a rigorously structured framework for dynamic client selection and radio resource allocation in heterogeneous wireless edge environments. Each FL round is formulated as a latency-aware scheduling problem that jointly captures local computation time, uplink transmission time, minimum participation constraints, and resource block assignment. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that integrates computation-aware, channel-aware, and fairness-aware scoring with greedy resource block allocation guided by marginal completion time reduction. The study further provides a clear methodological structure, workflow visualization, literature-grounded justification, dataset documentation, and uncertainty-aware result reporting. Under the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces the average round completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. Standard deviation reductions of 70.59% and 80.77% further indicate improved round-to-round stability and more reliable training behavior. These results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks. Full article
(This article belongs to the Special Issue 5G and Next-Generation Communication Technologies)
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