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Future Internet
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Moving towards 6G Wireless Technologies—Volume II
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Moving towards 6G Wireless Technologies—Volume II

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2373

Special Issue Editors

Dr. Alessandro Raschellà

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: wireless network optimization; dynamic spectrum access in cognitive radio networks; software-defined network; internet of things (IoT); heterogeneous networks
Special Issues, Collections and Topics in MDPI journals
Dr. Michael Mackay

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: internetworking; software-defined network; NFV; edge systems, wireless communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fifth-generation (5G) wireless networks have been key enablers for information societies over the last few years. Now, the sixth generation (6G) of wireless networks is under development, and the research community is preparing for the intelligent information societies of 2030 and beyond, targeting even more ambitious performance targets, such as peak data rates from 1 Tb/s, enhanced spectrum and energy efficiency, and extremely low-latency communications. In this context, key technologies for this performance include mm-wave and THz communications, very large-scale antenna arrays (i.e., spatial modulation–MIMO), laser communications and visible-light communication (VLC), blockchain-based spectrum sharing, and artificial intelligence (AI)/deep machine learning (ML).

This Special Issue invites authors to submit original and innovative manuscripts on the design, development, testing, and evaluation of 6G-enabling solutions, including, but not limited to, the following: 6G-enabling technology-based protocols, architectures, and frameworks; AI/ML-based smart algorithms and access schemes; QoS/QoE provisioning methods; intelligent distributed collaboration platforms; new energy-harvesting technologies; and intelligent spectrum management using blockchains.

Dr. Alessandro Raschellà
Dr. Michael Mackay
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • beyond 5G and 6G technologies
  • 6G communications
  • performance analysis for energy efficiency
  • latency and quality of experience
  • mm-wave and THz communications
  • artificial intelligence and deep machine learning
  • new antenna array design
  • laser communications and visible-light communication
  • blockchain for 6G

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Related Special Issue

Published Papers (2 papers)

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25 pages, 7600 KiB  
Article
Optimizing Radio Access for Massive IoT in 6G Through Highly Dynamic Cooperative Software-Defined Sharing of Network Resources
by Faycal Bouhafs, Alessandro Raschella, Michael Mackay, Max Hashem Eiza and Frank den Hartog
Future Internet 2024, 16(12), 442; https://doi.org/10.3390/fi16120442 - 28 Nov 2024
Viewed by 816
Abstract
The Internet of Things (IoT) has been a major part of many use cases for 5G networks. From several of these use cases, it follows that 5G should be able to support at least one million devices per km2. In this [...] Read more.
The Internet of Things (IoT) has been a major part of many use cases for 5G networks. From several of these use cases, it follows that 5G should be able to support at least one million devices per km2. In this paper, we explain that the 5G radio access schemes as used today cannot support such densities. This issue will have to be solved by 6G. However, this requires a fundamentally different approach to accessing the wireless medium compared to current generation networks: they are not designed to support many thousands of devices in each other’s vicinity, attempting to send/receive data simultaneously. In this paper, we present a 6G system architecture for trading wireless network resources in massive IoT scenarios, inspired by the concept of the sharing economy, and using the novel concept of spectrum programming. We simulated a truly massive IoT network and evaluated the scalability of the system when managed using our proposed 6G platform, compared to standard 5G deployments. The experiments showed how the proposed scheme can improve network resource allocation by up to 80%. This is accompanied by similarly significant improvements in interference and device energy consumption. Finally, we performed evaluations that demonstrate that the proposed platform can benefit all the stakeholders that decide to join the scheme. Full article
(This article belongs to the Special Issue Moving towards 6G Wireless Technologies—Volume II)
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20 pages, 1730 KiB  
Article
Time-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications
by Wataru Takabatake, Yohei Shibata, Kenji Hoshino and Tomoaki Ohtsuki
Future Internet 2024, 16(9), 332; https://doi.org/10.3390/fi16090332 - 11 Sep 2024
Viewed by 971
Abstract
There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous [...] Read more.
There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution. Full article
(This article belongs to the Special Issue Moving towards 6G Wireless Technologies—Volume II)
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