Advanced Technologies in AI-Assisted 5G/6G Networking

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

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 2896

Special Issue Editors


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Leading Guest Editor
Electrical and Electronics Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey
Interests: cellular wireless communications; self-organizing networks; machine learning; energy-efficient wireless networking

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Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: wireless communications; digital signal processing; self-organizing networks
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow, UK
Interests: 5G and Beyond networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is a truism that the number of connected devices and amount of data consumed per device have been on a dramatic rise for wireless communication networks, the reasoning for this being manifold. First, the Internet of Things (IoT) technology, bringing a promise of connecting essentially everything (e.g., home appliances, traffic lights, wearable devices, etc.) to the internet, places the scalability issue on the table, as the number of connected devices is likely to unprecedentedly increase. With the advent of technology, on the other hand, more demanding applications, including 4K video streaming, online gaming, a tactile internet, remote surgery, and augmented/virtual reality, have already enacted their debut, stretching the limits of current wireless communication technologies. In this regard, various advanced technologies have recently been studied within the scope of 5G and 6G connectivity, such as, UAV-assisted networking, intelligent reflective surface (IRS)-based communications, millimeter wave (mmWave ) and terahertz (THz) communications, visible light communications, massive MIMO systems, holographic-type communication, etc. As of today’s knowledge and understanding, most of these promising technologies will be available in 5G or 6G, with mmWave having already been included in 5G New Radio as Frequency Range-2, and the idea of using THz communications for 6G is widely accepted in the wireless communications research community.

One important nature of the next-generation mobile communication networks is for them to be data-driven, meaning the systems would be managed in a data-oriented manner, one of multiple reasons, the most intuitive aspect, for that being that the amount of data generated has been increasing with an unprecedented rate. The IoT concept is the main cause for that, as it offers to connect a diverse set of devices to the internet, translating to a rise in the amount of data generated, which can then be used in favor of wireless communication networks with the help of artificial intelligence (AI) by extracting the valuable and useful information. This, in turn, helps determine more informed decisions and act proactively. AI has been applied in various domains, from agriculture to finance, but it has a special place in wireless communications as well due to the fact that it is able to cause the processes to be more efficient, effective, agile, and dynamic. In 6G, for example, AI has been considered a main component of the ecosystem, since intelligence will play a vital role in the next-generation wireless networks.

Furthermore, when it comes to the optimization of wireless networks, conventional methods will be insufficient, given that the main characteristics of wireless communication networks is being quite dynamic in nature, meaning that the circumstances and conditions change very fast and frequently. In addition, there are multiple random effects, including shadowing, noise, interference, etc., causing the network to be much less predictable and almost impossible to obtain a model of such environments. This challenges the conventional optimization techniques as they usually require the model of the environment in advance, with it being hard for them to adopt to altering environments. An AI-based optimization, especially through reinforcement learning, could be designed model-free, thereby not requiring the model of the environment for optimization, since an experience created by AI is much more adaptable to changes.

This Special Issue aims to generate a collective understanding of how AI could be helpful in building advanced technologies for next-generation wireless communication networks, particularly 5G and 6G, as well as to reveal the potentials of AI in optimizing, managing, and enabling the wireless networks. Lastly, it is also important to demonstrate the applicability and feasibility of the aforementioned technologies for 5G and 6G networks, and their interoperability with AI. In this Special Issue, original research articles and reviews are welcome, research areas including (but not limited to) the following:

  • AI-assisted UAV-based communications for capacity enhancement and emergency scenarios;
  • The role of AI in intelligent reflective surface (IRS)-based communications;
  • V2V, V2I, and V2X communications;
  • Millimeter wave communications;
  • THz communications;
  • Context-aware wireless networking;
  • AI-assisted PHY and networking;
  • Green communications;
  • Visible light communications;
  • AI-assisted MAC layer solutions for 5G and 6G;
  • The help of AI in faster-than-Nyquist (FTN) signaling;
  • The integration of big data and AI for wireless networks;
  • Intelligent nobility management mechanisms for 5G and 6G;
  • 6G satellite communications.

We look forward to receiving your contributions.

Dr. Metin Öztürk
Dr. Sajjad Hussain
Prof. Dr. Muhammad Ali Imran
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • 5G
  • 6G
  • THz communications
  • mmWave
  • visible light communications
  • green communications
  • V2V, V2I, and V2X communications
  • satellite communications
  • intelligent reflective surfaces

Published Papers (1 paper)

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Research

19 pages, 1233 KiB  
Article
Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles
by Jianfeng Yang, Jianling Hu and Tianqi Yu
Electronics 2022, 11(22), 3658; https://doi.org/10.3390/electronics11223658 - 09 Nov 2022
Cited by 12 | Viewed by 1931
Abstract
The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious [...] Read more.
The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious network intrusion. The malicious intruders can take over the compromised ICVs and mediately intrude into the ICVs connected through IoV. Therefore, it is urgent to develop IVN intrusion detection methods for IoV security protection. In this paper, a ConvLSTM-based IVN intrusion detection method is developed by leveraging the periodicity of the network message ID. For training the ConvLSTM model, a federated learning (FL) framework with client selection is proposed. The fundamental FL framework works in the client-server mode. ICVs are the local clients, and mobile edge computing (MEC) servers connected to base stations (BSs) function as the parameter servers. Based on the framework, a proximal policy optimization (PPO)-based federated client selection (FCS) scheme is further developed to optimize the model accuracy and system overhead of federated ConvLSTM model training. Simulations are conducted by the exploitation of real-world IoV scenario settings and IVN datasets. The results indicate that by exploiting the ConvLSTM, the model size and convergence time are dramatically reduced, and the 95%-beyond detection accuracy is maintained. The results also unveil that the PPO-based FCS scheme outperforms the benchmarks on the convergence rate, model accuracy, and system overhead. Full article
(This article belongs to the Special Issue Advanced Technologies in AI-Assisted 5G/6G Networking)
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