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Private fifth generation (5G) networks have emerged as a cornerstone for ultra-reliable, low-latency connectivity across mission-critical domains such as industrial automation, healthcare, and smart cities. Compared to conventional technologies like 4G or Wi-Fi, they provide clear advantages, including enhanced service continuity, higher reliability, and customizable security controls. However, these benefits come with new security challenges, particularly regarding the confidentiality, integrity, and availability of data and services. This article presents a review of security vulnerabilities in private 5G networks. The review pursues four objectives: (i) to identify and categorize key vulnerabilities, (ii) to analyze threats that undermine core security principles, (iii) to evaluate mitigation strategies proposed in the literature, and (iv) to outline gaps that demand further investigation. The findings offer a structured perspective on the evolving threat landscape of private 5G networks, highlighting both well-documented risks and emerging concerns. By mapping vulnerabilities to mitigation approaches and identifying areas where current solutions fall short, this study provides critical insights for researchers, practitioners, and policymakers. Ultimately, the review underscores the urgent need for robust and adaptive security frameworks to ensure the resilience of private 5G deployments in increasingly complex and high-stakes environments.

Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency.

Security is considered a critical issue in the deployment of 5G networks because of the vulnerability of information that can be intercepted by eavesdroppers in wireless transmission environments. Thus, physical layer security has emerged as an alternative for the secure enabling of 5G technologies and for tackling this security issue. In this paper, we study the secrecy energy efficiency (SEE) in a downlink underlying cognitive radio (CR)—non-orthogonal multiple access (NOMA) system with a cooperative relay. The system has an energy-harvesting (EH) user and an eavesdropper, where the transmitter provides direct communication with a close secondary user and a distant secondary user via the relay. Our objective is to maximize the SEE of the CR-NOMA system under the constraints of a minimum information rate for the secondary users, a minimum amount of energy harvested by the EH user, and maximum power availability at the transmitter and the relay that still prevents them from causing unacceptable interference with the primary user. The proposed solution to maximize the SEE is based on the low-computational—complexity particle swarm optimization (PSO) algorithm. For validation purposes, we compare the optimization outcomes obtained by the PSO algorithm with the optimal exhaustive search method. Furthermore, we compare the performance of our proposed CR-NOMA scheme with the conventional orthogonal multiple access (OMA) scheme.

The expansion of 5G infrastructure and the deployment of large antenna arrays are set to substantially influence electromagnetic field (EMF) exposure levels within mobile networks. As a result, the accurate measurement of EMF exposure and the integration of EMF exposure constraints into radio resource management are expected to become increasingly important in future mobile communication systems. This paper provides a comprehensive review of EMF exposure evaluation frameworks for 5G networks, considering the impacts of high-energy beams, the millimeter wave spectrum, network densification and reconfigurable intelligent surfaces (RISs), while also examining EMF-aware radio resource management strategies for 5G networks and beyond, with RIS technology as an assistive factor. Furthermore, challenges and open research topics in the EMF evaluation framework and EMF-aware resource management for 5G mobile networks and beyond are highlighted. Despite the growing importance of RIS technology in enhancing mobile networks, a research gap remains in addressing specific EMF exposure considerations associated with RIS deployments. Additionally, the impact of EMF-aware radio resource allocation approaches on RIS-assisted 5G networks is still not fully understood.

Private 5G networks offer exclusive, secure wireless communication with full control deployments for many clients, such as enterprises and campuses. In these networks, edge computing plays a critical role by hosting both application services and the User Plane Functions (UPFs) as containerized workloads close to end devices, reducing latency and ensuring stringent Quality of Service (QoS). However, edge environments often face resource constraints and unpredictable failures such as network disruptions or hardware malfunctions, which can severely affect the reliability of the network. In addition, existing redundancy-based UPF resilience strategies, which maintain standby instances, incur substantial overheads and degrade resource efficiency and scalability for the applications. To address this issue, this study introduces a novel design that enables quick detection of UPF failures and two failover mechanisms to restore failed UPF instances either within the cluster hosting the failed UPF or across multiple clusters, depending on that cluster’s resource availability and health. We implemented and evaluated our proposed approach on a Kubernetes-based testbed, and the results demonstrate that our approach reduces UPF redeployment time by up to 37% compared to baseline methods and lowers system cost by up to 50% under high-reliability requirements compared to traditional redundancy-based failover methods. These findings demonstrate that our design can serve as a complementary solution alongside traditional resilience strategies, offering a particularly cost-effective and resource-efficient alternative for edge computing and other constrained environments.

Device-to-device (D2D) communication is one of the eminent promising technologies in Beyond Fifth Generation (B5G) wireless networks. It promises high data rates and ubiquitous coverage with low latency, energy, and spectral efficiency among peer-to-peer users. These advantages enable D2D communication to be fully realized in a multi-hop communication scenario. However, to ideally implement multi-hop D2D communication networks, the routing aspect should be thoroughly addressed since a multi-hop network can perform worse than a conventional mobile system if wrong routing decisions are made without proper mechanisms. Thus, routing in multi-hop networks needs to consider device mobility, battery, link quality, and fairness, which issues do not exist in orthodox cellular networking. Therefore, this paper proposed a mobility, battery, link quality, and contention window size-aware routing (MBLCR) approach to boost the overall network performance. In addition, a multicriteria decision-making (MCDM) method is applied to the relay devices for optimal path establishment, which provides weights according to the evaluated values of the devices. Extensive simulation results under various device speed scenarios show the advantages of the MBLCR compared to conventional algorithms in terms of throughput, packet delivery ratio, latency, and energy efficiency.

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

End-to-End (E2E) services in beyond 5G (B5G) networks are expected to be built upon resources and services distributed in multi-domain, multi-technology environments. In such scenarios, key challenges around multi-domain management and collaboration need to be tackled. ETSI Zero-touch network and Service Management (ZSM) architectural framework provides the structure and methods for effectively delivering E2E network services. ZSM pursues cross-domain automation with minimum human intervention through two main enablers: Closed Control Loop (CCL) and Artificial Intelligence (AI). In this work, we propose a multi-domain ZSM-based architecture aiming at B5G scenarios where several per-domain CCLs leverage Machine Learning (ML) methods to collaborate in E2E service management tasks. We instantiate the architecture in the use case scenario of multi-domain automated healing of Dynamic Adaptive Streaming over HTTP (DASH) video services. We present two ML-assisted techniques, first to estimate a Service Level Agreement (SLA) violation through a Edge-based Quality of Experience (QoE) Probe, and second to identify the root cause at the core transport network. Results from the experimental evaluation in an emulation environment using real mobile network traces point to the potential benefits of applying ML techniques for QoS-to-QoE estimation at Multi-Access Edge Computing facilities and correlation to faulty transport network links. Altogether, the work contributes towards a vision of ML-based sandbox environments in the spirit of E2E service and network digital twins towards the realization of automated, multi-domain CCLs for B5G.

The mobile industry is developing and preparing to deploy the fifth-generation (5G) networks. The evolving 5G networks are becoming more readily available as a significant driver of the growth of IoT and other intelligent automation applications. 5G’s lightning-fast connection and low-latency are needed for advances in intelligent automation—the Internet of Things (IoT), Artificial Intelligence (AI), driverless cars, digital reality, blockchain, and future breakthroughs we haven’t even thought of yet. The advent of 5G is more than just a generational step; it opens a new world of possibilities for every tech industry. The purpose of this paper is to do a literature review and explore how 5G can enable or streamline intelligent automation in different industries. This paper reviews the evolution and development of various generations of mobile wireless technology underscores the importance of 5G revolutionary networks, reviews its key enabling technologies, examines its trends and challenges, explores its applications in different manufacturing industries, and highlights its role in shaping the age of unlimited connectivity, intelligent automation, and industry digitization.

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.