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Future Internet
Special Issues
Moving Towards 6G Wireless Technologies—2nd Edition
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Moving Towards 6G Wireless Technologies—2nd Edition

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 3661

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 (4 papers)

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Research

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29 pages, 1763 KiB  
Article
Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach
by Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi and Masoud Kaveh
Future Internet 2025, 17(4), 169; https://doi.org/10.3390/fi17040169 - 11 Apr 2025
Viewed by 318
Abstract
The Internet of Things (IoT) has revolutionized modern communication systems by enabling seamless connectivity among low-power devices. However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. Cell-free massive multiple-input multiple-output (CF m-MIMO) has [...] Read more.
The Internet of Things (IoT) has revolutionized modern communication systems by enabling seamless connectivity among low-power devices. However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. Cell-free massive multiple-input multiple-output (CF m-MIMO) has emerged as a promising solution for IoT networks, offering enhanced spectral efficiency, low-latency communication, and robust connectivity. Nevertheless, balancing EE and security in such systems remains a significant challenge due to the stringent power and computational constraints of IoT devices. This study employs secrecy energy efficiency (SEE) as a key performance metric to evaluate the trade-off between power consumption and secure communication efficiency. By jointly considering energy consumption and secrecy rate, our analysis provides a comprehensive assessment of security-aware energy efficiency in CF m-MIMO-based IoT networks. To enhance SEE, we introduce a hybrid deep-learning (DL) framework that integrates convolutional neural networks (CNN) and long short-term memory (LSTM) networks for joint EE and security optimization. The CNN extracts spatial features, while the LSTM captures temporal dependencies, enabling a more robust and adaptive modeling of dynamic IoT communication patterns. Additionally, a multi-objective improved biogeography-based optimization (MOIBBO) algorithm is utilized to optimize hyperparameters, ensuring an improved balance between convergence speed and model performance. Extensive simulation results demonstrate that the proposed MOIBBO-CNN–LSTM framework achieves superior SEE performance compared to benchmark schemes. Specifically, MOIBBO-CNN–LSTM attains an SEE gain of up to 38% compared to LSTM and 22% over CNN while converging significantly faster at early training epochs. Furthermore, our results reveal that SEE improves with increasing AP transmit power up to a saturation point (approximately 9.5 Mb/J at PAPmax=500 mW), beyond which excessive power consumption limits efficiency gains. Additionally, SEE decreases as the number of APs increases, underscoring the need for adaptive AP selection strategies to mitigate static power consumption in backhaul links. These findings confirm that MOIBBO-CNN–LSTM offers an effective solution for optimizing SEE in CF m-MIMO-based IoT networks, paving the way for more energy-efficient and secure IoT communications. Full article
(This article belongs to the Special Issue Moving Towards 6G Wireless Technologies—2nd Edition)
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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 1083
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—2nd Edition)
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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
Cited by 1 | Viewed by 1116
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—2nd Edition)
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Review

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21 pages, 1334 KiB  
Review
A Survey of 5G Core Network User Identity Protections, Concerns, and Proposed Enhancements for Future 6G Technologies
by Paul Scalise, Michael Hempel and Hamid Sharif
Future Internet 2025, 17(4), 142; https://doi.org/10.3390/fi17040142 - 25 Mar 2025
Viewed by 435
Abstract
Fifth-Generation (5G) cellular networks extensively utilize subscriber identifiers throughout the protocol stack, thereby linking subscribers to their activities on the network. With the inherent use of linked identifiers comes the potential capability to track subscribers’ location and behavior, which poses critical challenges for [...] Read more.
Fifth-Generation (5G) cellular networks extensively utilize subscriber identifiers throughout the protocol stack, thereby linking subscribers to their activities on the network. With the inherent use of linked identifiers comes the potential capability to track subscribers’ location and behavior, which poses critical challenges for user identity protections and privacy in sensitive applications like military or healthcare operating over public 5G infrastructure. The reliance on such personal identifiers threatens a user’s right to privacy and brings to light the importance of proper mechanisms to mitigate these risks for current and future cellular network technologies. In this paper, we explore the 5G specifications to understand the most important list of identifiers and their use across Virtual Network Functions (VNF), and points of exposure within the Core Network (CN). We also examine the existing literature regarding identity protections and efforts to mitigate privacy concerns targeted in the CN. Findings include the need for a trust relationship between users and their network providers to protect and safeguard their identity. While 5G technology has greater user identity protections compared to previous cellular generations, our analysis shows that several areas of concern remain, particularly in the exchange of subscriber metadata. This work also finds that new technologies adopted in 5G networks add further complexity to maintaining a strict posture for safeguarding user identity and privacy protections. This paper also reviews the scientific community’s proposed enhancements for future 6G networks’ user identity and privacy protections, with a focus on emerging Artificial Intelligence (AI) and Machine Learning (ML) applications. The ethical implications of private or anonymous communications are also carefully weighed and examined to understand the multifaceted nature of this topic. Our work is concluded by proposing important further research to reduce the prevalence and reliance on personal identifiers such as the SUPI (Subscription Permanent Identifier) within 5G Core operations to help better protect user identity. We also propose replacing the widespread use of the SUPI between VNFs with ephemeral identifiers, building upon efforts by 3GPP aiming for 5G to protect the SUPI from eavesdroppers. Full article
(This article belongs to the Special Issue Moving Towards 6G Wireless Technologies—2nd Edition)
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