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Advances in 5G and Beyond Mobile Communication

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 3715

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Special Issue Editors

Dr. Ioannis Bartsiokas

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Guest Editor

Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, 15780 Athens, Greece
Interests: communication systems; machine learning; trustworthiness; autonomy
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Dr. Panagiotis K. Gkonis

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Guest Editor

Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Evripus Campus, 34400 Euboea, Greece
Interests: wireless communications; massive MIMO systems; 5G/B5G/6G networks; MIMO systems; ML-based radio resource allocation (RRM)
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Dr. George Vardoulias

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Guest Editor

Hellenic Naval Academy, 18539 Piraeus, Greece
Interests: tactical wireless networks; Iot cybersecurity; federated learning systems for the defense sector; the application of advanced DSP; machine learning techniques in predictive maintenance for naval rotating equipment; SDN; SDR; 5G; machine learning; tactical networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of Fifth-Generation (5G) mobile networks has revolutionized the landscape of telecommunications by enabling ultra-reliable low-latency communications (URLLCs), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTCs). As the global deployment of 5G networks accelerates, research is already advancing into the Beyond-5G (B5G) and Sixth-Generation (6G) domains, where the emphasis lies on ultra-intelligent, pervasive, and adaptive wireless ecosystems that support emerging applications such as the metaverse, autonomous mobility, smart factories, and remote medical interventions.

This Special Issue, hosted by the journal Electronics, aims to collect high-quality and original contributions that explore breakthroughs in mobile communication technologies spanning across theoretical models, simulation studies, experimental platforms, and system-level evaluations. We particularly welcome contributions that address challenges in spectrum efficiency, massive connectivity, end-to-end latency reduction, and physical layer security through the integration of intelligent and software-defined technologies.

In this context, we encourage submissions that focus on Reconfigurable Intelligent Surfaces (RISs) and Stacked RIS architectures for dynamic wireless environment reconfiguration, Orthogonal Time Frequency Space (OTFS) modulation for high-mobility and delay-Doppler channels, and Cell-Free Massive MIMO deployments to enhance uniform coverage and scalability. Furthermore, we seek novel research at the intersection of wireless sensing and communication, the development of AI/ML-aided solutions for Radio Resource Management (RRM), and the realization of edge-native intelligence through Federated Learning frameworks.

The Special Issue serves as a multidisciplinary platform uniting researchers from academia, industry, and defense, who aim to contribute to the progression of wireless communications. Survey articles that consolidate state-of-the-art technologies and outline future challenges are also welcome.

Topics of interest for this Special Issue include:

AI/ML and Deep Reinforcement Learning for B5G resource optimization;
Reconfigurable Intelligent Surfaces (RISs) and Stacked RIS-assisted communications;
Cell-Free Massive MIMO and user-centric topologies;
OTFS modulation for high-mobility and high-Doppler environments;
Federated Learning for decentralized and privacy-preserving communication;
Physical Layer Security for mission-critical wireless systems;
Wireless Sensing and Integrated Sensing and Communication (ISACs);
Software-Defined Networking (SDN) and Virtualized RAN in 5G/B5G;
Maritime, aerial, and satellite-integrated non-terrestrial networks (NTNs);
Energy-efficient MAC and PHY layer designs for future networks;
Beamforming and AI-aided beam selection in mmWave and THz bands;
Semantic and Goal-Oriented Communications;
Joint Communication and Radar Sensing (JCRS);
UAV-assisted, RIS-aided, and hybrid intelligent wireless topologies;
Quantum Communication-enabled protocols in 6G;
Blockchain for secure and decentralized wireless systems;
6G-native spectrum access schemes and intelligent reflecting MIMO (IR-MIMO);
Large Intelligent Surfaces (LIS) and Near-field MIMO;
AI-based digital twin modeling of wireless networks;
Joint Source-Channel Coding for real-time video over B5G links;
Neuromorphic and Spiking Neural Network models in PHY-layer design;
Intelligent MAC protocols for ultra-dense and delay-sensitive environments;
Multi-agent learning for cooperative wireless networks;
End-to-end ML pipelines for real-time adaptation in mobile networks.

You may choose our Joint Special Issue in Network.

Dr. Ioannis Bartsiokas
Dr. Panagiotis K. Gkonis
Dr. George Vardoulias
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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 and beyond (B5G) networks
6G communications
reconfigurable intelligent surfaces (riss)
stacked RISs
cell-free massive MIMO
orthogonal time frequency space (OTFS)
AI/ML for wireless communications
deep reinforcement learning (DRL)
federated learning
radio resource management (RRM)
integrated sensing and communication (ISAC)
beamforming and beam selection
semantic communications
physical layer security
software-defined networking (SDN)
edge intelligence
digital twin for networks
wireless sensing
non-terrestrial networks (NTNs)
UAV-assisted communications
THz and mmWave systems
quantum communications in 6G
neuromorphic wireless systems
intelligent MAC and cross-layer design
blockchain for wireless security

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Published Papers (6 papers)

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Research

22 pages, 1608 KB

Open AccessArticle

Joint Optimization for Uplink/Downlink Intelligent Decoupled Access in Heterogeneous C-V2X Communications

by Luofang Jiao, Pin Li, Yuhao Yang, Linghao Xia, Qiang Cheng, Ang Liu, Jingbei Yang and Xianzhe Xu

Electronics 2026, 15(10), 2046; https://doi.org/10.3390/electronics15102046 - 11 May 2026

Abstract

The uplink/downlink (UL/DL) decoupled access, which allows users to associate with different base stations (BSs), including small BSs (SBSs) and macro BSs (MBSs), has emerged as a network architecture in heterogeneous cellular vehicle-to-everything (C-V2X) communications. It can be tailored to mitigate the signal interference and attenuation impairments that cell-edge vehicles face, while vehicles closer to a BS can opt for coupled access. Therefore, a UL/DL intelligent decoupled access network that integrates decoupled and coupled access approaches is urgently needed for C-V2X communications. In this paper, we present a novel framework for UL/DL intelligent decoupled access in C-V2X networks in the context of fifth-generation mobile communications (5G) and beyond 5G (B5G). We propose a joint optimization approach for radio resource allocation, power control, and user association to enhance the network throughput of UL and DL while meeting the service quality requirements of vehicle users. Specifically, we formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and transform it into a standard convex optimization problem by introducing various auxiliary variables. An efficient iterative algorithm based on successive convex optimization techniques is introduced to obtain a sub-optimal solution. The proposed framework uniquely integrates decoupled and coupled access modes within a unified optimization formulation, enabling dynamic mode selection based on network load. Extensive simulation results demonstrate a significant performance improvement of the proposed UL/DL intelligent decoupled access in C-V2X networks compared with benchmark schemes. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

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20 pages, 1187 KB

Open AccessArticle

Radio Frequency Resonate and Fire (RF-RAF) Neurons Supporting Device Classification

by David L. Weathers, Michael A. Temple and Brett J. Borghetti

Electronics 2026, 15(10), 2023; https://doi.org/10.3390/electronics15102023 - 9 May 2026

Viewed by 74

Abstract

Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability is demonstrated here using recently introduced Radio Frequency Resonate-and-Fire (RF-RAF) neurons and eight WirelessHART devices. Performance is evaluated for RF-RAF-generated fingerprints against the established Gabor Transform (GTX) baseline using three classifier architectures: Random Forest (RndF), Convolutional Neural Network (CNN), and a Time-Incremented Spiking Neural Network (TI-SNN). The results show that RF-RAF fingerprints achieve an average classification accuracy of 96.7% across all three classifier types and consistently outperform GTX fingerprints at all evaluated fingerprint sizes. This performance persists under time-span-matched conditions, and the RF-RAF versus GTX benefit is not solely attributable to input data utilization. The TI-SNN surpasses 94% classification accuracy using M = 4 time step RF-RAF fingerprints with approximately 100 spikes per inference—a 4× larger GTX fingerprint requires approximately 1000 spikes to achieve the same classification accuracy. RF-RAF fingerprints offer two additional benefits: they are natively non-negative, which supports efficient neuromorphic hardware implementation, and they provide greater flexibility in fingerprint size selection. It is concluded that RF-RAF neurons provide an efficient neuromorphic-native encoding pathway for device RFF discrimination and offer improved accuracy–efficiency tradeoffs in training and inference for various classifier architectures. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

26 pages, 3269 KB

Open AccessArticle

Secrecy Performance of MIMOME Communications in Low-Altitude Economic Networking with Keyhole Channels

by Xujie Zang and Hongwen Yang

Electronics 2026, 15(8), 1712; https://doi.org/10.3390/electronics15081712 - 17 Apr 2026

Viewed by 198

Abstract

Ensuring physical layer security for low-altitude economic networking (LAENet) is critical due to the broadcast nature of wireless channels. In dense urban environments, multi-antenna LAENet systems are often impaired by the keyhole effect, which induces rank deficiency and poses significant security challenges. This paper investigates the secrecy performance of a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system in LAENet with keyhole channels. Depending on the availability of channel state information (CSI) at the transmitter, three wiretap scenarios are considered: (i) broadcasting, (ii) passive eavesdropping, and (iii) spoofing. For each scenario, the optimal precoder is designed to maximize the secrecy transmission rate. Based on these designs, we derive closed-form expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR). To provide insights into the effect of keyholes on secrecy diversity order and array gain under this severe rank-deficiency structure, we also obtain asymptotic expressions for SOP and ASR in the high signal-to-noise ratio (SNR) regime using the Mellin transform. Numerical results validate the analytical expressions and illustrate the influence of key parameters on secrecy performance. These findings provide meaningful guidance for the secure design of future LAENet deployments. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

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17 pages, 484 KB

Open AccessArticle

A Federated Learning-Based Network Intrusion Detection System for 5G and IoT Using Mixture of Experts

by Loukas Ilias, George Doukas, Vangelis Lamprou, Spiros Mouzakitis, Christos Ntanos and Dimitris Askounis

Electronics 2026, 15(5), 1057; https://doi.org/10.3390/electronics15051057 - 3 Mar 2026

Viewed by 670

Abstract

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter

α

to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

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19 pages, 5155 KB

Open AccessArticle

Evaluation of the Digital Data Performance Transmission of a Two-User Fiber-Radio System with NOMA Wireless Access Scheme

by Rodrigo Cuevas-Terrones, Josefina Castañeda-Camacho, José Eligio Moisés Gutiérrez-Arias, Mauricio Rodríguez and Ignacio Enrique Zaldívar-Huerta

Electronics 2025, 14(22), 4357; https://doi.org/10.3390/electronics14224357 - 7 Nov 2025

Viewed by 701

Abstract

This paper presents a set of simulations to evaluate the digital data transmission performance of a two-user Fiber-Radio system that shares the same optical and wireless link. For this purpose, at the optical stage, the Optisystem software 21.1.0 is used to simulate the transmission of two digital signals at a bit rate of 2.4 Gbps through a 100 m single-mode standard fiber (SM-SF). To implement the use of a single optical link for two users, the Wavelength Division Multiplexing (WDM) technique is used. Subsequently, the wireless stage is evaluated in MATLAB R2023a by using the NOMA scheme, and the wireless transmission and data recovery are shown and explained in detail. In the wireless stage, four factors that affect the transmitted signal are considered: noise, two types of fading, and Co-channel interference. Performance of the wireless system is evaluated using statistical tests. The simulation set has potential applications in the performance evaluation of Fiber-Radio systems for outdoor environments due to their satisfactory operation at distances of up to 500 m from the transmitting station. Furthermore, the proposed system could be applied to cellular telephony, the Internet of Things (IoT), and sensor signal transmissions for industrial or agricultural uses. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

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18 pages, 1018 KB

Open AccessArticle

An Event-Based Time-Incremented SNN Architecture Supporting Energy-Efficient Device Classification

by David L. Weathers, Michael A. Temple and Brett J. Borghetti

Electronics 2025, 14(18), 3712; https://doi.org/10.3390/electronics14183712 - 19 Sep 2025

Viewed by 1392

Abstract

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF fingerprinting. Using experimentally collected WirelessHART transmissions, the TI-SNN achieves classification accuracy above 90% while reducing fingerprint density by nearly seven-fold and spike activity by over an order of magnitude compared to a baseline Rate-Encoded SNN (RE-SNN). These reductions translate to significant potential energy savings while maintaining competitive accuracy relative to Random Forest and CNN baselines. The results position the TI-SNN as a step toward a fully neuromorphic “RF Event Radio” capable of low-latency, energy-efficient device discrimination at the edge. Full article

(This article belongs to the Special Issue Advances in 5G and Beyond Mobile Communication)

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