ANALYSIS OF NB-IOT TECHNOLOGY DEPLOYMENT SCENARIO IN 4G NETWORKS USING SIMULATION RESULTS

The significant volume of data traffic, ultimate data rates, reduced latency, more transmissions with efficient energy, spectrum consumption, and novel technological use cases must all be considered using the fifth generation (5G) mobile network. Future 5G networks will connect billions of objects, known as the Internet of Things (IoT) or massive Machine-T Communications (mMTC), in addition to standard Mobile Broadband (MBB) services. Non-Terrestrial Networks (NTNs) may serve connection requests anywhere and anytime by offering wide-area coverage and preserving service continuity, availability, and scalability. The satellite platform is a crucial NTN for integrating with the 5G network and delivering the necessary services. Soon, 5G network development will depend on satellite access, as stated in the 3Gpp Release 16 specification. This paper provides options for incorporating satellite systems into a 5G network design to increase capacity and extend coverage in 5G mobile networks. In addition, 5G networks must address a network model to resolve outage, congestion, and cell edge context issues. The last phase is the simulation of the problem formulation for the investigation of capacity and traffic in hybrid satellite-terrestrial 5G mobile backhauling networks.

With the development of communication technology, the key technologies of the 5th Generation Mobile Communication Network have been solved and the basic communication protocols have been adopted. The transmission of network information is also more convenient, bringing revolutionary changes to people's lives. At present, China's 5G mobile communication network has gradually become popular and covered. In the last two years, 5g technology will surely bring great changes to people in the field of mobile Internet and Internet of things, 5G mobile networks can support iot requirements. The 5g technology has not got rid of the fragility of virtual network while providing convenience, so it faces the security pressure which cannot be underestimated. And the security of 5G network and service should cover all aspects of service, let the user use it after ensuring the security, build a reliable, safe, convenient bridge, otherwise it is easy to cause the loss of user's benefit, even endanger national security, thus seriously hinder the development of 5G technology.

The architecture of a multi-collaborative mobile telemedicine system operating over third-generation (3G) mobile networks is presented. The system architecture is based on advanced signaling protocols that allow multimedia multi-collaborative conferences in IPv4/IPv6 3G scenarios. It is designed to communicate the personnel of an ambulance with medical specialists in a remote hospital through a 3G mobile access, providing appropriate support for real-time transmission of medical data and videoconference, together with other non real-time services. The system has been optimized specifically to operate over 3G mobile networks using the most appropriate codecs and quality of service (QoS) mechanisms to improve the services offered

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.

The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.

The highly computationally capable mobile devices and the continuously increasing demand for high data rates and mobility, which are required by several mobile network services, enabled the research on fifth-generation (5G) mobile networks that are expected to be deployed beyond the year 2020 in order to support services and applications with more than one thousand times of today’s network traffic. On the other hand, the huge and complex location-aware datasets exceed the capability of spatial computing technologies. In this direction, the mobile cloud computing (MCC) technology was introduced as the combination of cloud computing and mobile computing, enabling the end-users to access the cloud-supported services through mobile devices (e.g., smartphones, tablets, portable computers or wearable devices). The mobile applications exploit cloud technologies for data processing, storage and other intensive operations, as they are executed on resource providers external to the devices. This tutorial article is a comprehensive review of the current state-of-the-art and the latest developments on mobile cloud computing under the 5G era, which helps early-stage researchers to have an overview of the existing solutions, techniques and applications and investigate open research issues and future challenges in this domain.

The development of 6G mobile networks will produce many smart Internet of Things devices and data at the network’s edge. Advanced AI technology holds the potential to enable 6G mobile networks to collect and analyze this data for innovative applications and intelligent services. However, the inherent privacy constraints and limited communication resources within 6G mobile networks often make direct data transmission to servers undesirable. Federated learning (FL) is seen as a promising approach to address these problems. Yet, integrating FL into 6G mobile networks presents data heterogeneity issues. In this article, we design and propose a privacy-preserving FL scheme in 6 G mobile networks. The core idea of this scheme is to take both dataset size and the discrepancy between local and global category distributions into consideration to compute the weights of different clients and apply threshold Paillier cryptosystem to perform weighted aggregation on client-encrypted data. Security analysis and experimental results demonstrate the advantages of this scheme in guaranteeing privacy preservation and improving training accuracy. Finally, we present some future directions for the integration of FL and 6G mobile networks.

The fourth generation of mobile networks (4G) must overcome the new challenges that have emerged with the explosive growth in demand for mobile traffic and the Internet of Things. Therefore, the OMNs (Operator Mobile Networks) should plan for upgrading 4G to 5G mobile network beyond 2020, which is the expected year for launching 5G. A techno-economic analysis and mathematic modeling approach was considered. Also, a new pricing model is proposed to be consistent with the growth of mobile broadband. The two major cost elements, Capital Expenditure and Operative Expenditure were compared with the revenue expected to be collected within the deployment period. The results show that 5G is very beneficial, not only because of its lower cost compared with 4G LTE, but also due to the increment of average data consumptions offered by 5G mobile technologies and the increasing growth of number of users. We also found that good analysis of Price Elasticity of Volume gives an important margin of benefit. We confirm that the reuse of existing sites have a large impact on reducing costs when a denser macro network deployed. However, we encountered a lack of the limited capacity related to the macro sites and coverage limited with small cell solutions like femtocells, picocells deployed with 5G mmW system and Wi-Fi.

The advent of sixth-generation (6G) mobile networks introduces unprecedented demands for ultra-low latency, terabit-per-second throughput, and dynamic resource allocation across heterogeneous services such as URLLC, eMBB, and mMTC. Traditional resource management approaches lack the adaptability and predictive intelligence required to operate under such conditions. This study proposes a comparative analysis of deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures—for intelligent resource allocation in 6G environments. Synthetic traffic patterns and benchmark datasets were used to emulate realistic 6G workloads, and models were evaluated using key performance metrics: accuracy, latency, throughput, energy efficiency, and fairness index. Simulations were conducted in Python using TensorFlow and PyTorch frameworks within SDN-based virtualized environments to replicate programmable 6G infrastructures. The proposed hybrid system, integrating CNN, RNN, and Transformer modules, achieved latency reduction of 38%, throughput improvement of 22%, energy savings of 26%, and fairness index gain of 18% compared to baseline models. These findings demonstrate the viability of deep learning for scalable, adaptive, and sustainable resource allocation in next-generation mobile networks. The study contributes a reproducible simulation framework, a performance benchmark across models, and a foundation for future research in federated learning, edge AI, and 6G deployments.

Next to higher data rates and lower latency, the upcoming fifth-generation mobile network standard will introduce a new service ecosystem. Concepts such as multi-access edge computing or network slicing will enable tailoring service level requirements to specific use-cases. In medical imaging, researchers and clinicians are currently working towards higher portability of scanners. This includes i) small scanners to be wheeled inside the hospital to the bedside and ii) conventional scanners provided via trucks to remote areas. Both use-cases introduce the need for mobile networks adhering to high safety standards and providing high data rates. These requirements could be met by fifth-generation mobile networks. In this work, we analyze the feasibility of transferring medical imaging data using the current state of development of fifth-generation mobile networks (3GPP Release 15). We demonstrate the potential of reaching 100Mbit/s upload rates using already available consumer-grade hardware. Furthermore, we show an effective average data throughput of 50Mbit/s when transferring medical images using out-of-the-box open-source software based on the Digital Imaging and Communications in Medicine (DICOM) standard. During transmissions, we sample the radio frequency bands to analyse the characteristics of the mobile radio network. Additionally, we discuss the potential of new features such as network slicing that will be introduced in forthcoming releases.

The scarcity of network resources and the contention between resources and traffic volume have been the most critical network performance bottlenecks due to the booming growth of various applications in mobile Internet and Internet of Things. Consequently, effectively matching traffic with resources is of great importance and poses significant challenges. The 5G mobile communications networks will be heterogeneous, dense, and smart with various resources autonomously matching with the traffic demands. Although various traffic offloading schemes have been extensively investigated, applications in 5G present new characteristics such as interference-awareness on licensed or unlicensed bands, autonomous spectrum utilization, and delay-tolerant or delay sensitive traffic. In this article, we focus on HUDNs, which comprise dense small cells on the licensed band, WiFi AP on the unlicensed band, D2D communications, and V2V communications coexisting together to address the ever-increasing performance demands on both the user and network sides. We first summarize the recent research findings in this area and the technical challenges. We further present emerging traffic offloading frameworks and discuss the implementation issues including traffic offloading from virtualization, user-centric caching, and network selection in V2V communications. Furthermore, we propose an autonomous traffic offloading scheme based on big data and machine learning and also highlight future research directions.

The fifth generation (5G) mobile network will enable the Internet of Things (IoT) to take a large leap into the age of future computing. As a result of extended connectivity, high speed, and reduced latency services being provided by 5G, IoT has experienced and will continue to undergo a remarkable transition in every field of daily life. Furthermore, fog computing will revolutionize the IoT platforms by decentralizing the operations through the cloud and ensuring sustainability with big data, mobility, and reduced processing lag. 5G is ubiquitous, reliable, scalable, and economic in nature. The features will not only globalize IoT in a broader spectrum, but also make common people interact smartly and efficiently with the environment in real time. In this study, a combined survey is presented on different IoT applications coupled with cloud platforms. Moreover, the capabilities of IoT in the influence of 5G are explored as well as how the IoT platform and services will adopt through 5G are envisaged. Additionally, some open issues triggered by 5G have been introduced to harness the maximum benefit out of this network. Finally, a platform is proposed to implement in the telepresence project based on the investigation and findings.

Machine-to-Machine communication is rapidly growing and becoming a significant part of the current 4G and future 5G mobile network data traffic, which is intended to provide coverage support and lower costs for mobile network providers. The 5G mobile network represents a promising technology to support the future of Machine-to-Machine communications. In recent years, smart devices, such as smartphones and traffic monitoring systems, have experienced exponential growth over mobile networks with different radio resource usage. This has caused massive challenges as a result of simultaneous access data traffic as well as a large number of devices sending small-sized data. This chapter proposes a novel data traffic aggregation and slicing model with algorithms in 5G uplink, based on classifying and measuring the data traffic to achieve quality of service for smart systems. Moreover, 5G radio resources are efficiently shared by several smart devices in a relay node by aggregating incoming data traffic based on quality of service.

It is expected that internet of things (IoT) will deal with the major activities in the connected living environment as well as the industrial processes. All these aspects are going to be real in the frameworks of the fifth-generation (5G) mobile networks. 5G-based narrowband IoT (NB-IoT) networks have the capability to serve various innovative IoT applications at a great extent. NB-IoT is third generation partnership project (3GPP) standardized low power wide area (LPWA) technology which is designed for IoT devices requiring long battery life, low cost, worldwide coverage, and high system capacity. To improve the performance, 3GPP has agreed that the NB-IoT will continue evolving as part of the 5G specifications. NB-IoT along with 5G will work in several connected living applications. This combination will also be very useful in the industrial environments which need high data rates and low latency. All these features will be supported by 5G in the future. Similarly, applications with low data rates in the IoT world will be supported by NB-IoT. So 5G and NB-IoT are going to be a popular combination for several new applications.

The use of bandwidth for mobile and cellular networks has been increasing at an unprecedented rate, with applications in emergent areas such as smart vehicles and civil infrastructure, telemedicine, and remote education. The predicted increase of mobile network utilisation requires a high data rate capacity to transfer information through the network. The current limited capacity of the fourth generation (4G) mobile network is not expected to satisfy the predicted high data demand by 2020. To meet this requirement, a new fifth generation (5G) mobile network has been proposed. In order to reach the high capacity demands, the 5G requires a wide frequency bandwidth, which is not available in the sub-3 GHz frequency spectrum. Therefore, many international organisations have assigned the millimetre-wave band (mm-Wave), which includes 24, 28, 37, 39, and 60 GHz, as a frequency band for 5G. Using mm-Wave raises many challenges such as an increase in the path loss, radiation field absorbed by the human body, signal polarisation misalignment, and MIMO antenna footprint inside the mobile handset. Therefore, designing beam-steering, reconfigurable circular polarization, and compact MIMO antenna systems for 5G applications is important to cope with these challenges. Although there are some antenna systems were reported recently for mm-Wave 5G, they had limited steering angles, one polarization types or bulky antenna size. This thesis aims to solve the current challenges mentioned above by developing mm-Wave 5G antenna systems and in doing so makes three main contributions to the field of mm-Wave 5G antenna systems.As the path loss and radiated fields absorbed by surface layers of the human body increase at mm-Wave, emphasis is given into the development of the beam-steering antenna system, which is the first contribution of this thesis. The beam-steering technique aims at reducing the effects of electromagnetic field exposure on the human head, as well as maintain a consistent connection between the handset and the base station. A novel compact three-dimensional (3-D) beam-steering system of mobile handsets at 28GHz is proposed. It consists of dipole antenna arrays, power dividers and digital phase shifters. Nevertheless, a novel technique of connecting p-i-n diodes to build a phase shifter is proposed for better performance. The proposed design has the ability to steer the beam in a wide angle in 3-D. it relies on using both sides of a printed circuit board for a two-dimensional antenna array. Moreover, to further improve the performance of the beam-steering system, a tunable power divider for mm-Wave 5G is presented. It is used to tune the feeding amplitude of the beam-steering antenna array, and thus reduces the side-lobe level (SLL) of the antenna radiation pattern.n nThe second contribution is the development of two reconfigurable circular polarisation (CP) structures for 5G applications at mm-Wave. CP is an effective technique to mitigate signal polarisation misalignment, phase deviation and multipath effect problems. Electrical reconfigurable methods are used to switch the CP type by using p-i-n diodes. Firstly, an open-ended stub concept with a simple feed circuit is used to build a novel reconfigurable CP patch antenna. Then the same concept is developed to build a reconfigurable CP antenna array system using branch-line couplers, power divider, and 4 p-i-n diodes. The open-ended stub concept is found to be efficient in both designs by reducing the complexity and the number of the p-i-n diode switches compared to the reported existing CP antenna systems in the literature.MIMO is a major technology of 4G mobile antenna system and also has disruptive potential for mm-Wave 5G. Therefore, the third contribution is the development of new design approaches to integrate the current 4G with futurist 5G MIMO antenna systems. Integrating these two technologies in one structure leads to a reduction of the total MIMO antenna footprint significantly within handsets, where the available space is limited. To that end, two novel 5G/4G MIMO antenna systems are built. First, 5G/4G MIMO antenna system is proposed by using one structure; and it covers multiband mm-Wave spectrums (28, 37 and 39) GHz for 5G as well as sub 3 GHz for 4G.n Moreover, to further improve the gain especially at mm-Wave band, an antenna array structure is deployed to build another 5G/4G MIMO antenna system. This design integrates 5G and 4G operations into a single structure; and covers a wideband 23-29 GHz for 5G and frequency-tuning 2.05-2.70 GHz for 4G.The developed antenna systems reported in this thesis positively contribute to the antenna design domain and are expected to encourage safety, compactness, reliability, and a multiband antenna system for the mm-Wave 5G industry in near future.n