Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service.

The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings. By merging Convolutional Neural Networks (CNN) for feature extraction and Reinforcement Learning (RL) for decision-making, DRARLCNN optimizes resource allocation, minimizing latency and maximizing Quality of Service (QoS). Utilizing a state-of-the-art "5G Resource Allocation Dataset", the research employs Python, TensorFlow, and OpenAI Gym to implement and test the model in a simulated 5 G environment. Results demonstrate the effectiveness of DRARLCNN, showcasing an impressive R2 score of 0.517, MSE of 0.035, and RMSE of 0.188, surpassing existing methods in allocation efficiency and latency. The DRARLCNN model not only outperforms existing methods in allocation efficiency and latency but also sets a new benchmark for future research in dynamic 5G resource allocation. Through its innovative approach and promising results, DRARLCNN opens avenues for further advancements in optimizing resource allocation within dynamic 5G networks.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms.

Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023.

Context-Based e2e Autonomous Operation in B5G Networks.

The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.

Dynamic Coalition Formation among IoT Service Providers: A Systematic Exploration of IoT Dynamics Using an Agent-Based Model.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

The Advantage of the 5G Network for Enhancing the Internet of Things and the Evolution of the 6G Network.

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network.

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

Advancing Digital Twin-Based Collision Avoidance: A Comprehensive Analysis of Communication Networks for Safety-Critical Applications in Industry 4.0.

This study presents a theoretical framework for defining the performance level of wireless safety functions within industrial environments. While acknowledging the simplifications inherent in our approach-primarily based on packet loss rates as a measure of system performance-the study underscores the dynamic challenges posed by real-world warehouses. Through an in situ measurement study of a forklift truck safety system, we validate the proposed method and emphasize the need for a more nuanced examination of wireless communication in complex settings. The study advocates for an expanded theoretical framework that considers fluctuations in warehouse dynamics, accounting for their impact on packet loss rates and, consequently, the precision of performance-level assessments. Furthermore, the research highlights the complexity introduced by wireless system characteristics not addressed in the simplified model, urging future investigations to incorporate these factors for a comprehensive understanding of wireless safety systems. The absence of specific criteria for wireless systems within existing standards emphasizes the necessity for a specialized framework in addressing safety aspects unique to wireless applications.

Cost-Efficient Network Planning for the Cross-Border Baltic Corridor-A Study.

Performing 5G coverage planning across borders introduces real-life challenges related to legalities, intercountry agreements, and binding documents. This work provides RF network modelling exercise results to provide uninterrupted 5G coverage to the Via Baltica and Rail Baltica transport corridors crossing Estonia and Latvia and on the border with Lithuania, as well as the Tallinn-Tartu-Valga and Valka-Valga roads (Latvia-Estonia), capable of cross-border 5G services. The study starts by identifying and interviewing stakeholders from different sectors of operation in the Baltic states and Europe and then provides an overview of some of the main legal acts and documents regulating the electronic communications sector in the Baltic states and Europe. Furthermore, 5G network requirements are proposed. In addition, the necessary and existing passive and active infrastructure is described, including spectrum management-related analysis, where the RF bands 700 MHz and 3500 MHz are analysed. Finally, coverage planning is performed. The network modelling results aim to foresee the number of new sites that need to be built on the said transport corridors, also examining the existing infrastructure for such purposes. Additionally, an estimated timeline for building the new sites is provided.

Blockchain Protocols and Edge Computing Targeting Industry 5.0 Needs.

"Industry 5.0" is the latest industrial revolution. A variety of cutting-edge technologies, including artificial intelligence, the Internet of Things (IoT), and others, come together to form it. Billions of devices are connected for high-speed data transfer, especially in a 5G-enabled industrial environment for information collection and processing. Most of the issues, such as access control mechanism, time to fetch the data from different devices, and protocols used, may not be applicable in the future as these protocols are based upon a centralized mechanism. This centralized mechanism may have a single point of failure along with the computational overhead. Thus, there is a need for an efficient decentralized access control mechanism for device-to-device (D2D) communication in various industrial sectors, for example, sensors in different regions may collect and process the data for making intelligent decisions. In such an environment, reliability, security, and privacy are major concerns as most of the solutions are based upon a centralized control mechanism. To mitigate the aforementioned issues, this paper provides the opportunities for and highlights some of the most impressive initiatives that help to curve the future. This new era will bring about significant changes in the way businesses operate, allowing them to become more cost-effective, more efficient, and produce higher-quality goods and services. As sensors are getting more accurate, cheaper, and have lower time responses, 5G networks are being integrated, and more industrial equipment and machinery are becoming available; hence, various sectors, including the manufacturing sector, are going through a significant period of transition right now. Additionally, the emergence of the cloud enables modern production models that use the cloud (both internal and external services), networks, and systems to leverage the cloud's low cost, scalability, increased computational power, real-time communication, and data transfer capabilities to create much smarter and more autonomous systems. We discuss the ways in which decentralized networks that make use of protocols help to achieve decentralization and how network meshes can grow to make things more secure, reliable, and cohere with these technologies, which are not going away anytime soon. We emphasize the significance of new design in regard to cybersecurity, data integrity, and storage by using straightforward examples that have the potential to lead to the excellence of distributed systems. This groundbreaking paper delves deep into the world of industrial automation and explores the possibilities to adopt blockchain for developing solutions for smart cities, smart homes, healthcare, smart agriculture, autonomous vehicles, and supply chain management within Industry 5.0. With an in-depth examination of various consensus mechanisms, readers gain a comprehensive understanding of the latest developments in this field. The paper also explores the current issues and challenges associated with blockchain adaptation for industrial automation and provides a thorough comparison of the available consensus, enabling end customers to select the most suitable one based on its unique advantages. Case studies highlight how to enable the adoption of blockchain in Industry 5.0 solutions effectively and efficiently, offering valuable insights into the potential challenges that lie ahead, particularly for smart industrial applications.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium.

Fixed wireless access (FWA) provides a solution to compete with fiber deployment while offering reduced costs by using the mmWave bands, including the unlicensed 60 GHz one. This paper evaluates the deployment of FWA networks in the 60 GHz band in realistic urban and rural environment in Belgium. We developed a network planning tool that includes novel backhaul based on the IEEE 802.11ay standard with multi-objective capabilities to maximise the user coverage, providing at least 1 Gbps of bit rate while minimising the required network infrastructure. We evaluate diverse serving node locations, called edge nodes (EN), and the impact of environmental factors such as rain and vegetation on the network design. Extensive simulation results show that defining a proper EN's location is essential to achieve viable user coverage higher than 95%, particularly in urban scenarios where street canyons affect propagation. Rural scenarios require nearly 75 ENs per km2 while urban scenarios require four times (300 ENs per km2) this infrastructure. Finally, vegetation can reduce the coverage by 3% or increment infrastructure up to 7%, while heavy rain can reduce coverage by 5% or increment infrastructure by 15%, depending on the node deployment strategy implemented.

A Feasibility Study of a Traffic Supervision System Based on 5G Communication.

At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review.

The existing sub-6 GHz band is insufficient to support the bandwidth requirement of emerging data-rate-hungry applications and Internet of Things devices, requiring ultrareliable low latency communication (URLLC), thus making the migration to millimeter-wave (mmWave) bands inevitable. A notable disadvantage of a mmWave band is the significant losses suffered at higher frequencies that may not be overcome by novel optimization algorithms at the transmitter and receiver and thus result in a performance degradation. To address this, Intelligent Reflecting Surface (IRS) is a new technology capable of transforming the wireless channel from a highly probabilistic to a highly deterministic channel and as a result, overcome the significant losses experienced in the mmWave band. This paper aims to survey the design and applications of an IRS, a 2-dimensional (2D) passive metasurface with the ability to control the wireless propagation channel and thus achieve better spectral efficiency (SE) and energy efficiency (EE) to aid the fifth and beyond generation to deliver the required data rate to support current and emerging technologies. It is imperative that the future wireless technology evolves toward an intelligent software paradigm, and the IRS is expected to be a key enabler in achieving this task. This work provides a detailed survey of the IRS technology, limitations in the current research, and the related research opportunities and possible solutions.

Integrity Verification of Distributed Nodes in Critical Infrastructures.

The accuracy and reliability of time synchronization and distribution are essential requirements for many critical infrastructures, including telecommunication networks, where 5G technologies place increasingly stringent conditions in terms of maintaining highly accurate time. A lack of synchronization between the clocks causes a malfunction of the 5G network, preventing it from providing a high quality of service; this makes the time distribution network a very viable target for attacks. Various solutions have been analyzed to mitigate attacks on the Global Navigation Satellite System (GNSS) radio-frequency spectrum and the Precision Time Protocol (PTP) used for time distribution over the network. This paper highlights the significance of monitoring the integrity of the software and configurations of the infrastructural nodes of a time distribution network. Moreover, this work proposes an attestation scheme, based on the Trusted Computing principles, capable of detecting both software violations on the nodes and hardware attacks aimed at tampering with the configuration of the GNSS receivers. The proposed solution has been implemented and validated on a testbed representing a typical synchronization distribution network. The results, simulating various types of adversaries, emphasize the effectiveness of the proposed approach in a wide range of typical attacks and the certain limitations that need to be addressed to enhance the security of the current GNSS receivers.

Multi-Connectivity for 5G Networks and Beyond: A Survey.

To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks.

The rapid growth of mobile data traffic will lead to the deployment of Ultra-Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)-based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three-dimensions (3D), with dominant of line-of-sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method.

In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.

Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity.

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.