Toward a Trust-Centric Semantic Mobile Core Leveraging Quantum Security

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration.

Next Generation Network (NGN) represents a fundamental paradigm shift in the wireless communication which uses packet switching instead of circuit switching. NGN is an architecture that provides seamless integration of both new and traditional telecommunication services across high-speed packet networks. Heterogeneous network (HN) is the group of network which integrated with wired, wireless and sensory network with internet connectivity. Combination of HN and NGN (HNGN) will offers highly reliable services at all situation. Implementing protocols such as TCP is much complicate in the heterogeneous network environment. The objective of this paper is providing an assured, reliable data transmission in the HNGN. Data transmission in HNGN renders ineffective communication due to barrier when implementing different network protocols. The proposed methodology, called Reliable Barrier-Free Services (RBS) optimizes the reliability of HNGN.

The increasing complexity of Radio Access Network (RAN) environments, especially 5G and future 6G infrastructures, has prompted the development of smarter and more flexible network automation infrastructures. As a more advanced form of context-driven decision-making and process automation in wireless networks, Large Language Models (LLMs) have recently been refined using Retrieval-Augmented Generation (RAG). This paper reviews current developments in applying RAG-augmented LLMs to RAN automation, including spectrum and power allocation, fault detection in distributed RANs, and secure 5G/6G multi-agent automation. It also presents comparative studies with more conventional approaches, such as Deep Reinforcement Learning (DRL), and discusses multi-agent systems, graph-based retrieval mechanisms, and agentic AI systems. The review highlights potential limitations, including safety concerns, data management challenges, and scalability issues, as well as future research and implementation directions. The discussion demonstrates the disruptive potential of RAG-enhanced LLMs in reshaping automation and intelligence in next-generation wireless networks.

One of the key features of the the next generation of networks is the coexistence of multiple radio access technologies such as WIFI, WIMAX and LTE. This heterogeneous environment provides to the mobile users the possibility to use different services at any time and any where. Various multiple attribute decision making (MADM) algorithms have been proposed to manage the terminal mobility while ensuring the best continuity of services. Among the most MADM methods which are widely used for solving the network selection problem in the research literature are SAW, MEW, TOPSIS, GRA, VIKOR, DIA, E-TOPSIS and FADM. This paper evaluates the performance of eight MADM methods, that aim to ensure the seamless network selection under the principle always best connected.

In the Next Generation Networks (NGNs) users will carry multiple devices forming cooperative networks known as Personal Area Networks (PANs). Some existing technologies enable this type of networks, such as Bluetooth or IEEE 802.15.4, but a unified framework capable of self-organizing them dynamically in a full heterogeneous environment populated by these and other technologies still has to be defined. Also, these networks are envisioned to be connecting dynamically to the Internet, and may use two IP versions and their autoconfiguration mechanisms.In this paper we propose a new framework, the Autoconfiguration and Self-management of Personal Area Networks (ASPAN), which enables the automatic and dynamic deployment of PANs in the heterogeneous environments envisioned for NGNs and handles the automatic and dynamic connection of a PAN to the global Internet.

This article examines the transformative impact of autonomous vehicles, drones, and artificial intelligence/machine learning technologies on modern supply chain management. It explores how these technologies are revolutionizing traditional logistics operations through enhanced efficiency, cost reduction, and sustainability. The article provides a comprehensive analysis of the technical foundations, economic impacts, and operational benefits of autonomous vehicles in transportation, the applications of drone technology in warehouse operations and last-mile delivery, and the role of AI/ML as the cognitive backbone of supply chain automation. Furthermore, it addresses integration challenges, including technological barriers, workforce transformation requirements, and ethical considerations, while offering strategic recommendations for organizations and identifying research gaps for future scholarly inquiry. The findings suggest that these technologies collectively enable fundamental operational transformations, shifting supply chains from linear to networked structures, from reactive to predictive decision-making, and from fragmented to integrated information flows, ultimately leading to more resilient, sustainable, and intelligent supply networks.

To survive in today's competitive environment, a service provider must continually evolve its network and enable new revenue-generating services faster and more cost effectively than the competition. These services must create value for the end user, and they must be delivered with simplicity and ease of simple point-and-click provisioning. Next-generation network architectures that enable these services require intelligent optical networks and multiservice networks to be tightly linked and share a common control plane. Unified signaling and control-plane mechanisms that span across both service and transport networking domains are essential to achieve convergence of the multiservice and optical networking layers. This paper serves as a prelude to a series of papers presented in this issue that examine various challenges associated with implementing a service-driven next-generation network. Topics covered include network planning, engineering network architectures, management, control-plane mechanisms, and service provisioning.

User mobility, heterogeneity of networks and network technologies, variety of mobile devices (e.g., different operating systems, display size, CPU capabilities, battery limitations), and wide range of video-centric applications (e.g., video on demand, video games, live video streaming, video conferences, surveillance) open up the demand for user-centric solutions that adapt the video application to the underlying network conditions and device characteristics. In addition, the absence of battery improvements suitable to meet the growing power requirements and the need for green Information and Communications Technologies ICT provides strong motivation for researchers to develop energy efficient techniques to manage and reduce power consumption in next-generation wireless networks, while still meeting high user quality expectations. In this context, we propose adapt-or-handover, a hybrid multimedia delivery solution, which balances the benefits of multimedia content adaptation and of network selection to decrease power consumption in a heterogeneous wireless network environment. The proposed solution is analyzed and comparatively tested through simulations. The results show how, by using adapt-or-handover, the users benefit from up to 31% energy savings with insignificant degradation in quality, in comparison with other energy efficient solutions.

Among the key issues of next-generation wireless networks (NGWN), mobility management is one of the most crucial. In fact, provision of seamless roaming and services continuity is required for multimedia applications and particularly in presence of heterogeneous wireless environments. Although several IP-based mobility protocols have been proposed in the literature, they are not able to guarantee seamless roaming, especially for real-time applications. This paper proposes an efficient handoff protocol, called enhanced handoff protocol for integrated networks (eHPIN). This protocol carries out localized mobility management, fast or anticipated handoff, and access network discovery in order to alleviates services disruption during roaming in heterogeneous IPv6-based wireless environments. Simulation results show benefits of eHPIN in term of signaling traffic overhead cost, end-to-end throughput, handoff latency and packet loss compared to existing IPv6-based mobility management protocols.

Mobile next-generation networks (NGNs) are a necessary element in reaching the goal of truly ubiquitous computing. The Universal Mobile Telecommunication System, the third-generation mobile service concept, is a technology step to mobile NGNs. We can view NGNs as a merger of the Internet and intranets with mobile networks and with media and broadcast technologies. The Universal Mobile Telecommunication System (UMTS) is-from a radio perspective-a third-generation cellular technology, which is defined by the International Telecommunication Union (ITU) in its IMT-2000 framework (2001). From a conceptual point of view, it represents a technology step to mobile NGNs' facilitating ubiquitous computing. Researchers conceived UMTS to combine Internet protocol (IP) and mobile technologies to offer personal communication and personalized content everywhere. Its goal is to apply Internet protocols for mobile services control and end-to-end applications. By analyzing the trends of key technologies, we can see how they drive the evolution of the Internet and mobile communications toward mobile NGNs. In this article, I describe the IP-based approach of UMTS for the provision of mobile multimedia services. The integration of WLAN into the UMTS architecture could indicate that the field is moving toward separating mobile and fixed Internet.

We consider the problem of signaling rate and performance for control and management of reconfigurable intelligent surfaces (RISs) in next-generation mobile networks. To this end, we first analytically determine the rates of RIS reconfigurations and handover using a stochastic geometry network model. We derive closed-form expressions of these rates, while taking into account static obstacles (both known and unknown), self-blockage, RIS location density, and variations in the angle and direction of user mobility. Based on the derived rates, we analyze the signaling rates of a sample novel signaling protocol, which we propose as an extension of the current handover signaling protocol. We evaluate the signaling overhead due to RIS reconfigurations and the related energy consumption. We also provide a capacity planning analysis of the related RIS control plane server for its dimensioning in the network management system. The results quantify the impact of known and unknown obstacles on the RIS reconfiguration rate and the handover rate as a function of device density and mobility. We evaluate the scalability of the model, the related signaling overhead, energy efficiency, and server capacity in the control plane. To the best of our knowledge, this is the first analytical model to derive the closed form expressions of RIS reconfiguration rates, along with handover rates, and relate its statistical properties to the signaling rate and performance in next-generation mobile networks.

The global expansion of 5G and the approaching 6G era are pushing conventional single-mode fibers toward their fundamental capacity limits, necessitating a paradigm shift in optical network infrastructure. This study introduces a novel, AI-optimized tri-concentric-core fiber with an optimized grading profile (TCC-OGP) to overcome this capacity crunch through spatial-division multiplexing (SDM). The fiber design was realized through an integrated artificial intelligence framework, combining a neural network surrogate model with particle swarm optimization to efficiently navigate a complex multi-objective design space. The resultant TCC-OGP fiber supports six spatial-division-multiplexed LP modes, achieving a breakthrough in the traditional capacity–nonlinearity trade-off. A comprehensive numerical analysis demonstrates that the proposed structure achieves 92% of the theoretical Shannon capacity while simultaneously suppressing nonlinear impairments by 65% compared to the standard single-core fiber. Furthermore, the fiber exhibits low differential mode delay, a flattened dispersion of approximately 16 ps/(nm·km) at 1550 nm, strong bend tolerance (<0.01 dB/m at a 30 mm radius), and excellent inter-modal crosstalk below −25 dB over 20 km. These performance metrics confirm the TCC-OGP fiber’s suitability for terabit-scale transmission in metro networks, dense 5G back-haul, and future 6G infrastructures, establishing a scalable and intelligent platform for next-generation optical networks.

This comprehensive collection of articles explores the evolution and strategic implementation of MIMO technologies, focusing on LS-MIMO and mMIMO systems. These systems play pivotal roles in advancing 5G and prospective 6G networks. The articles span a broad spectrum, from theoretical foundations to practical deployment strategies. They explore significant technological innovations such as spectral and energy efficiencies, addressing both current and emerging challenges in the wireless communication sector. The discussion begins with the transition from traditional small-scale MIMO systems to more sophisticated LS-MIMO configurations, highlighting key innovations like "clean-slate" approaches and advanced beamforming techniques tailored for heterogeneous networks. Subsequent articles build on this foundation, offering in-depth analyses of practical application scenarios, system performance enhancements, and the integration of cutting-edge technologies such as terahertz communication and machine learning within Massive MIMO frameworks. The articles highlight the importance of addressing real-world challenges, including pilot contamination, channel estimation issues, and the optimization of CSI acquisition. The synthesis of research demonstrates the significant potential of MIMO technologies to enhance network capacity and system reliability. Additionally, it outlines the necessary adaptations for their successful integration into future telecommunications infrastructure. The collective insights from these studies provide a robust framework for ongoing technological advancement, emphasizing the need for continuous innovation and strategic deployment of MIMO technologies. This body of work guides future research and implementations, ensuring MIMO technology remains at the forefront of telecommunications evolution and drives the capabilities of next-generation wireless networks.

The advent of 6G wireless communication systems presents new opportunities for leveraging artificial intelligence (AI) and machine learning (ML) techniques to enable intelligent networking. This paper explores the application of AI and ML in intelligent networking for 6G, aiming to enhance network performance, optimize resource management, and improve user experience. However, the application of AI and ML in 6G networks also presents significant challenges. The design and implementation of intelligent networking algorithms must address issues such as scalability, real-time decision-making, energy efficiency, privacy, and the need for robust learning in dynamic and heterogeneous network environments. The paper provides a brief understanding of the potential benefits and challenges of integrating AI and ML in 6G networks and provides valuable insights for researchers in shaping the future of intelligent networking for 6G wireless communication systems.

Generalized multi-protocol label switching (GMPLS), which supports all types of switching, can provide next-generation mobile broadband IP networks with quality of service (QoS) and traffic engineering (TE). Future core network of wireless mobile system will be the GMPLS-based wired IP network; GMPLS must provide an efficient scheme to support micro-mobility in wireless environment. The purpose of this paper is to propose a scheme of integrating GMPLS with micro-mobility for the next-generation mobile broadband IP networks. The proposed scheme can re-establish a label switched path (LSP) rapidly when a mobile node (MN) enters a new subnet. Therefore, low latency performance can be achieved. The resource utilization in GMPLS networks can also be improved.