Researchers from both academia and industry have started exploring the potential of sixth generation cellular networks, envisioning novel concepts and futuristic capabilities. An empirical analysis of real-world fifth generation (5G) deployments serves a compass to direct the next stage of evolution and provides insights on the additional improvements required for the future services and applications. However, acquiring real-world measurement data at a city or county scale poses substantial challenges in terms of time and cost. To address this issue, this paper presents a practical and cost-effective data collection testbed and a methodology that harnesses the existing services provided by municipal council authorities, including curbside waste collections to generate large-scale realtime network coverage maps. Rich datasets of measurement data collected from multiple fourth generation (4G) and 5G cells over seven months in Nottingham, United Kingdom (UK) for all four major UK network operators, namely EE, Vodafone, O2, and Three Mobile, are provided. These large datasets can be utilized for analyzing network deployment options, coverage, and future service provisioning as well as designing and training artificial intelligence and machine learning algorithms to further optimize the mobile networks. In addition, the paper reviews the latest empirical 5G network analysis tools and techniques, which were not seen in previous generations.

The increasing demand for emerging vehicular services, such as immersive entertainment, safety applications, and enhanced infotainment, has driven the development of Vehicle-to-Everything communication. However, vehicular networks face significant challenges due to stringent Quality of Service (QoS) requirements and the highly dynamic nature of wireless environments. Traditional Radio Access Network (RAN) architectures struggle to adapt to these conditions, necessitating more flexible and intelligent solutions. Open RAN, with its virtualised and intelligent architecture, offers a promising approach by incorporating Artificial Intelligence and Machine Learning for real-time network optimisation. This paper proposes a Deep Reinforcement Learning (DRL)-based Modulation and Coding Scheme (MCS) selection algorithm within an Open RAN-enabled vehicular network to optimise resource usage while ensuring QoS compliance. The proposed algorithm leverages the flexibility of Open RAN and the adaptability of DRL to dynamically configure MCS parameters, enhancing the Quality of Experience for users in challenging vehicular scenarios. Simulation results demonstrate that the DRL-based approach reduces network resource usage by 33% compared to conventional SNR-based MCS selection while improving QoS satisfaction by approximately 5%.

Network slicing enables 5G/6G networks to support ultrareliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). However, while this virtual networking technology enhances network efficiency, it also adds substantial signaling overhead. Maintaining submillisecond latency and managing dense deployments require continuous signaling at high resolution, which keeps hardware components active, leading to increased energy consumption. In this article, we introduce a novel network controller that manages slice congestion and admission, designed to meet flexible Quality-of-Experience requirements for both priority and nonpriority traffic. Utilizing metadata from Internet of Things (IoT) device applications and network characteristics, we introduce adaptability and elasticity features, enabled by transfer and reinforcement learning, significantly lowering signaling overhead and network resources. Further, analytical results show the proposed framework effectively reduces rejection rates and congestions across varying mMTC and eMBB traffic loads.

Abstract Video conferencing and live streaming are being used in various industries, such as healthcare, gaming, telecommunication, manufacturing and others. As technology progresses, the need for real-time data transmission with minimal latency has increased. Web Real-Time Communication () addresses this need effectively. WebRTC is a technology designed to provide real-time communication through web and mobile browsers. Its low latency and P2P communication capabilities make it a convenient technology for secure, efficient communication in real-time applications. This paper reviews the key features of WebRTC, discusses its strengths and weaknesses and investigates a detailed analysis of 83 existing studies. Moreover, It evaluates all use cases that can be adopted by WebRTC by examining their descriptions, problem statements, and research gaps based on literature to date. Finally, It highlights the open research directions for the emerging technologies and enhancements of WebRTC. to identify their potential applications.

The 5G era has seen the largest number of studies around Radio Environment Maps (REM) than in the previous three generations combined. Visualization of network coverage on interactive maps provides contextual information and numerous benefits to operators, regulators and to the public. In this context, spatial interpolation and extrapolation techniques are used to add synthetic data points between measurements to fill gaps in the data, where techniques such as machine learning, Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) are used to enhance the quality of REMs. In this paper we present a state-of-the-art software package, which integrates a series of interpolation methods, augmented with polygon intersection queries functionality to control data used for estimation of coverage on the roads. The proposed web-based application is powered by a set of modular Python packages, making it future-proof and real-world ready, enabling efficient and precise network management.

Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges, and implementation strategies to harness its transformative potential effectively. This survey paper investigates the impact of Maritime Blockchain on Supply Chain Management, shedding light on its ability to enhance transparency, traceability, and overall efficiency in the complex realm of maritime logistics. Furthermore, the paper offers a practical roadmap for the integration of blockchain technology into the Maritime Industry, presenting a comprehensive framework that maritime stakeholders can adopt to unlock the advantages of blockchain in their operations. In addition to these aspects, the study conducts a thorough examination of the current network infrastructure in Ports and Vessels. This assessment provides a holistic view of the technological landscape within the maritime sector, which is crucial for understanding the challenges and opportunities for the successful implementation of blockchain technology. Moreover, the research identifies and analyzes specific Blockchain cybersecurity challenges that are pertinent to the Maritime Industry.

Despite 5 revolutionary generations and 18 releases, automated measurement tools for cellular networks offer limited programmability and their integrated APIs remain difficult to reproduce. We demonstrate the challenges and solutions related to building web and mobile-based applications for continuous cellular networks data collection and analysis. This is shown in the form of an integrated suite of reliable, low-cost cloud-based data processing, querying and analysis software functions that domain experts and laypeople users can utilize to assess cellular networks’ quality of service at street level.

Abstract Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN.

Non-Orthogonal Multiple Access (NOMA) is a promising technology for future-generation wireless systems, with potential to contribute to the improvement of spectral efficiency. NOMA groups users into clusters, based on channel gain-difference. However, user mobility continuously changes the channel gain, which often requires re-clustering. In this paper, we study a set of re-clustering methods: arbitrary, one-by-one and Kuhn-Munkres assignment algorithm (KMAA), that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to automatically dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. Experimental results show that the KMAA method improves efficiency and capacity through minimizing the number of re-clustering events, improving resource utilization, and lowering signaling overhead. Other sets of results highlight the throughput and outage probability gains of the KMAA method across a wide range of mobility scenarios. We also provide an analysis of the KMAA algorithm when applied to MIMO-NOMA, encompassing link resiliency and maintenance of average gain-difference, among users in clusters.

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools.

Network slicing enables 5G network operators to offer diverse services in the form of end-to-end isolated slices, over shared physical infrastructure. Wireless service providers are facing the need to plan and rapidly evolve their slices configuration to meet the varied tenants' demand. Network slicing unfolds a new market dimension to the infrastructure providers as well as to the tenants, who may acquire a network slice from the infrastructure provider to deliver a specific service to their respective subscribers. In this new context, there is a growing need for new network resource allocation algorithms to capture such proposition. This paper addresses this problem by introducing a family of online algorithms with the aim to (i) minimize tenants spectrum allocation costs, (ii) maximize radio resource utilization and (iii) ensure that the service level agreements (SLAs) provided to tenants are satisfied. We focus on improving the performance of prediction-based decisions that are made by a tenant when prediction models lack the desired accuracy. Our evaluations show that the proposed probabilistic approach can automatically adapt to prediction error variance, while largely improving network slice acquisition cost and resource utilization.

Non-Orthogonal Multiple Access (NOMA) system is considered as core enabler for the Fifth-Generation (5G) of the wireless cellular networks, contributing to the improvement of spectral efficiency. NOMA groups users into clusters, based on the maximum channel gain-difference. However, user mobility continuously changes the channel gain and requires re-clustering, depending on the percentage of mobile users and the environment in which they operate. In this paper, we propose a set of re-clustering methods: arbitrary, one-by-one and simultaneous, that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. The proposed methods are based on mathematical formulation and algorithms and address many technical and computational challenges associated with the clustering techniques. Numerical and experimental investigation has been carried out to test their performance and the results show that the simultaneous method can provide lower number of clusters, making it more suitable in dense and highly mobile scenarios. Our findings also demonstrate that this method has the potential to minimize the number of reclustering, improving resource utilization and lowering signaling loads.

This paper presents a mathematical approach to the future dynamic spectrum market, where multiple secondary operators compete to gain radio resources. The secondary network operators (SNOs) face various concurrent auctions. We discuss techniques, which can be used to select auctions to optimize their objectives and increase the winning probability. To achieve these goals, a matching problem is formulated and solved, where secondary operators are paired with auctions, which can provide spectrum with the highest expected quality of service (QoS). A total outlay optimization is structured for auctions with concealed reserve prices, which are only revealed to the secondary operators for some price upon request. More specifically, we solve a nonlinear problem to determine the minimum set of auctions by using the brute force algorithm. We further introduce a surplus maximization and demonstrate an auction mechanism of spectrum allocation by modifying the Bayesian-Nash equilibrium. The mathematical analyses highlight that the optimal choice is achievable through the proposed mathematical formulation.

Dynamic spectrum sharing aims to provide flexible spectrum usage and improve spectrum efficiency for cellular and noncellular networks. We propose two optimization models using stochastic optimization algorithms in which the secondary operator: 1) spends the minimal cost to achieve the target grade of service (GoS) assuming unrestricted budget or 2) gains the maximal profit to achieve the target GoS assuming restricted budget. We assume that there are spectrum resources available for secondary operators to borrow under a merchant mode. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Second, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators, respectively. We mathematically model the sharing agreement scenario and derive the closed-form solution of blocking probabilities for each operator. Results show how the secondary operator performs in terms of blocking probability under various offered loads and sharing capacity.

Although federated cloud computing has emerged as a promising paradigm, autonomous orchestration of resource utilization within the federation is still required to be balanced on the basis of workload assignment at a given time. Such potential imbalance of workload allocation as well as resource utilization may lead to a negative cloudburst within the federation. The analytical models found in the literature do not provide explicit framework to provide dynamic measure of workload requirement within a cloud federation environment. An additional challenge is the adoption of operational restrictions from regulatory body, the federation, or the federation participants. The analytical models presented in this paper have addressed workload balancing within a federated cloud environment under the access control restrictions agreed between federation members. The proposed analytical models provide a closed form solution for access probability and resource utilization at a given time. The analytical results are evaluated at different degree of security within the cloud federation environment and efficiency of the proposed workload balancing models is demonstrated. The proposed models can be used for cloud services dimensioning to handle high computational demand.

Dynamic Spectrum Sharing (DSS) aims to provide opportunistic access to under-utilized spectrum in cellular networks for secondary network operators. In this paper we propose an algorithm using stochastic and optimization models to borrow spectrum bandwidths under the assumption that more resources exist for secondary access than the secondary network demand by considering a merchant mode. The main aim of the paper is to address the problem of spectrum borrowing in DSS environments, where a secondary network operator aims to borrow the required spectrum from multiple primary network operators to achieve a maximum profit under specific grade of service (GoS) and budget restriction. We assume that the primary network operators offer spectrum access opportunities with variable number of channels (contiguous and/or non- contiguous) at variable prices. Results obtained are then compared with results derived from a heuristic algorithm in which spectrum borrowing are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than random counterpart.

Spectrum sharing between operators with exclusive licensing have become a major concern for mobile network operators and regulators to respond to the growing spectrum demand of the multimedia applications. One of the important issues in spectrum sharing is to determine the potential benefit when multi-operators share the resources under certain mutual agreements. The paper focuses on dynamic spectrum sharing management in next generation cellular networks. We propose three loss network models and derive the closed form expression of blocking probability, each having specific level of cooperation and interaction. The analytical frameworks are presented to analyze the benefits due to multi-operator cooperation for spectrum sharing. This quantifies the operators' gains and degradations of operators engaged in cooperative arrangements. We also analyze the overall network performance in terms of spectrum utilization and present a detailed comparisons between the proposed analytical frameworks.