Next-generation Mobile Networks Research Articles

Connected and Automated Mobility Services in 5G Cross-Border Environments: Challenges and Prospects

The next generation of mobile networks, namely 5G, promises significant qualitative and quantitative advances for multiple vertical domains. However, most studies and investigations assess these advances under the implicit assumption of a single network service provider, with typical national coverage. In this article, we take a close look at the automotive sector and highlight a series of challenges emerging in the context of its inherent (inter)national mobility and the corresponding importance of cross-border and/or multioperator environments. Our target is to pinpoint the key influential factors affecting the transition toward seamless (cooperative) connected and automated mobility services within and across national borders. To this end, we identify and analyze a series of challenges in the areas of, networking, application, security, and regulation. We further present and discuss a series of corresponding solutions investigated in the pragmatic context of our experimental activities.

6G Vision & Analysis of Potential Use Cases

As an operator driven global organization representing the entire value chain, Next Generation Mobile Networks (NGMN) has a substantial track record in providing industry-wide guidance on the 4th generation of cellular mobile networks (4G) and the 5th generation of cellular mobile networks (5G) development.

From earth to space: A first deployment of 5G core network on satellite

Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites. The new trend enables the paradigm shift of satellite-terrestrial integrated networks with global coverage. In particular, the integration of 5G communication systems and satellites has the potential to restructure next-generation mobile networks. By leveraging the network function virtualization and network slicing, the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks. We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility. We conducted experiments to validate the satellite 5G core network functions. The validated procedures include registration and session setup procedures. The results show that the satellite 5G core network can function normally and generate correct signaling.

Extremely Lightweight Constant-Round Membership-Authenticated Group Key Establishment for Resource-Constrained Smart Environments toward 5G

AbstractWith rapid development of next-generation mobile networks and communications (5G networks), group-oriented applications in resource-constrained smart environments (RSEs), such as smart homes and smart classrooms, have attracted great attentions. Due to the insecure communications between resource-constrained devices, secure group communications in RSE toward 5G face many challenges. In RSE toward 5G, lightweight communications and low computational overheads are crucial. Besides, the private tokens used to generate the group key are expected to be reused multiple times. However, the conventional frameworks for secure group communications cannot meet these requirements. A practical construction of extremely lightweight constant-round membership authenticated group key establishment framework is proposed in this paper for RSE toward 5G, which not only implements identity authentication among the members and group key establishment but also ensures extremely lightweight computation and communication costs by each group member. In our proposed scheme, the increase in the number of group members will not lead to a linear or logarithmic increase in the communication and calculation costs at the member side. Our framework also resists external and internal attacks and meets all the desirable security features. In this framework, the privacy of tokens can be well protected, so that they can be reused for multiple times. Therefore, our scheme significantly reduces the costs of communication and calculation, and it is more efficient compared with the related schemes in the literature. This proposal is fairly suitable for lightweight membership authentication and group key establishment in RSE toward 5G.

Opportunistic RIS-assisted rate splitting transmission in coordinated multiple points networks

Reconfigurable intelligent surfaces (RIS) have attracted a great deal of interests due to its potential contributions to the next-generation mobile networks. The deployment of RIS in multi-user wireless networks promises to reduce system hardware costs, signal processing complexity, as well as energy consumption due to its small size, lightweight and ability to actively shape the wireless propagation environment. Based on the ability to suitably adjust the phase shifts according to the dynamic wireless environment, the transmission rate and energy efficiency can be improved by deploying software-controlled meta-surfaces to reflect signals from the source to the destination. This paper proposes a RIS-assisted rate splitting-based transmission strategy in the Coordinated Multiple Points (CoMP) transmission network. The obtained results demonstrated that the proposed opportunistic RIS-assisted rate splitting transmission outperforms the conventional jointly transmission Non-Orthogonal Multiple Access (NOMA) strategy and the mutual-aided NOMA strategy in terms of outage probability under a target power level when the obstacle exists. Along with the one-off control RIS, the network spectral efficiency can be improved significantly by the proposed opportunistic RIS-assisted (O-RIS) rate splitting strategy under the target channel interference levels.

Assessing the Impact of Emerging Vertical Markets on 5G Diffusion Forecasting

5G technology is not only a next-generation mobile network used for human communications, but also provides a worthy solution to machine-to-machine (M2M) communications requirements that contribute to the digital transformation of many vertical industries including factories of the future, media and entertainment, eHealth, and energy, among others. The assessment of the impact of 5G technology adoption by these M2M vertical markets on the overall 5G service demand represents a challenge for telecommunication operators, policy makers, and regulators, as it is a key factor affecting the underlying techno-economic analysis used for the formulation of their strategic planning and competition policy shaping. Within this scope, in this study, a methodology for overall 5G service diffusion forecasting is proposed that takes into account the introduction of 5G technology in multiple vertical markets. This methodology is based on stochastic modeling of 5G service diffusion in every vertical market under study. It also employs Monte Carlo simulation to generate potential forecast paths for the aggregate 5G service demand. Study findings indicate that the emergence of vertical markets has a significant positive impact on the saturation point of overall 5G demand without increasing the service's diffusion uncertainty to the same degree.

SO-RAN: Dynamic RAN Slicing via Joint Functional Splitting and MEC Placement

Next-Generation (NG) mobile networks are expected to revolutionize wireless communication systems, facilitating the deployment of critical applications with stringent Quality of Service (QoS) requirements. Multi-access Edge Computing (MEC) and Radio Access Network (RAN) slicing are considered vital mechanisms of upcoming NG systems as they allow creation of end-to-end isolated RAN slices and increase of serving mobile data traffic on edge. The efficient deployment of RAN functions promises flexible and disaggregated architectures for the diversified needs of NG networks. In this work, we propose a novel optimization approach that minimizes RAN/MEC economic costs and maximizes served traffic. This framework creates isolated RAN slices by optimal placement of RAN and MEC functions per slice, which solves the problem of operating cost-efficient edge networks and increases the served traffic with diverse QoS requirements. We first formulate our framework as a convex and linear problem and then decompose it using a distributed method that accelerates the performance and guarantees the optimal global solution. Our results show that our proposed work better uses the spectrum, and there is a trade-off between the achieved throughput and the cost incurred to the network.

Deep Learning Approaches for Traffic Prediction Forecasting in Multi-Level Cloud Architecture for Autonomous Vehicle Services

Autonomous vehicle (AV) is one of the most new use cases and a technology for fifth-generation (5G) and next-generation mobile networks in numerous applications., the use of AVs has exponentially worldwide due to the rapidly growing awareness and use of artificial intelligence (AI) methods in various fields. Predicting data flows is essential for AVs to improve data transmission and decrease delays through more efficient use of appropriate capabilities, monitoring, management, and control of the traffic system. This paper proposes a deep learning approach (DL) with the bidirectional long-short-term memory model (BI-LSTM) for predicting the traffic rates of AVs with multi-cloud services. In terms of prediction accuracy, a comparison is conducted between the BI-LSTM and the unidirectional LSTM based on the number of batch sizes used. The prediction accuracy is computed using the root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and processing time. The results show that the prediction accuracy with BI-LSTM outperforms the LSTM model. Besides, the prediction accuracy using 8 batch sizes outperforms the competitors and offers outstanding performance.

Machine learning enhanced next-generation optical access networks—challenges and emerging solutions [Invited Tutorial

Optical access networks are envisioned to become increasingly complex as they support more and more diverse and immersive services, each with a different capacity, latency, and reliability need. While machine learning has been touted as a silver bullet that will intelligently manage network operations and resources to meet these demands, as it had been anticipated for core and metro networks, there exist various challenges that need to be addressed to progress machine learning models from research to production. In this tutorial, we first aim to motivate the continued push to advance optical access networks and rationalize the use of machine learning in these networks. We then highlight the challenges that are especially amplified due to the traffic dynamicity and heterogeneity, data scarcity, and computation-resource constraints of optical access networks. We discuss emerging machine learning approaches that are being explored to address these challenges. Finally, we consider a fast and self-adaptive machine learning enhanced dynamic bandwidth allocation scheme in an illustrative future use case of supporting immersive human-to-machine communications over the mobile fronthaul of next-generation mobile networks.

Joint user association and fair scheduling for dual connectivity heterogeneous networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks. Dual connectivity (DC) can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks. In this paper, we propose a joint user association and fair scheduling algorithm (JUAFS) to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks. Considering different coverage sizes, numbers of users, and quality of experience characteristics of heterogeneous cells, we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability. Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

Analysis of the impact of radio interference on the quality of service in 5G networks and the possibility of their minimization

This article is devoted to the analysis of the impact of radio interference on fifth-generation mobile communication networks and options for minimizing their impact. 5G networks are considered high-speed and high-load networks, which is why stability and performance are critical for effective service delivery in next-generation mobile networks, which in turn is achieved by combating or minimizing the effects of radio interference on the network. The article presents an overview of modern research and technologies aimed at detecting and eliminating radio interference in fifth-generation mobile communication networks. The article also considers the prospects and future directions of research in this field and is a valuable source of information for professionals in the field of wireless communications.

SiGe and CMOS Technology for State-of-the-Art Millimeter-Wave Transceivers

Innovation and evolution are paramount where the demand for wideband, data-intensive connectivity is ever-increasing, and the only constant is change. Standards that define the operation of next-generation mobile networks are moving away from the traditional radio frequency (RF) spectrum and into millimeter-wave (mm-wave) bands. The physical layer (PHY) for IEEE 802.11ad Wi-Fi and 802.11ay WLANs dictates operation in the unlicensed 60 GHz band. 5G New Radio (NR) applications utilize selected bands from 26 to 39 GHz. Additionally, newly developing Fourth Industrial Revolution (4IR) applications depend on 5G NR as an enabling technology. Satellite communications and wireless backhaul will occur in E-band between 70–86 GHz. Increasing demands from the market cause designers to push boundaries, and the development of standards guide technological advances. Perhaps the most substantial improvements are observed in integrated circuit technology. This article details the major Si processes, namely Complementary Metal Oxide Semiconductor (CMOS) and SiGe Bipolar CMOS (BiCMOS), and their foray into the wireless transceiver space traditionally dominated by GaAs. CMOS and BiCMOS have become popular in many communities because of their low fabrication cost and excellent digital integration capabilities. RF performance has matured to where Si is a serious competitor for tried-and-tested III-V technologies. Some extreme environments and sophisticated applications still favor GaAs and GaN, however. GaAs, for example, can yield unparalleled output power and excellent noise figure performance, albeit at a higher cost and increased design and manufacturing complexity due to the multi-chip nature of these circuits.

Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks.

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user's software platforms.

Two-tier MPC architecture for AGVs navigation assisted by edge computing in an industrial scenario

The next generation of mobile networks may be a bridge to move industrial control to edge computing. Embedded controllers physically installed in the industrial process can be replaced by software remotely executed as a service. In this context, Automated-Guided Vehicles (AGVs) are industrial agents that can benefit from this scenario by offloading computing-intensive tasks remotely to the edge. Additionally, an AGV control system based on remote software promotes flexibility, an essential requirement for Industry 4.0. Considering the remote control of AGV, the mobile robot may traverse areas where the signal is degraded, increasing the risks of collisions and accidents due to connection failures. Previous works suggest the adoption of Model Predictive Control (MPC) to control mobile robots in the occurrence of delays and packet losses. In this article, we propose a two-tier architecture of MPCs, one executed at the edge to plan the trajectory of multiple AGVs globally and the other executed individually in each AGV to keep it on the planned track. In simulations performed in an edge computing environment using a robot simulator, the vehicles follow the planned trajectory even with network degradation. In our simulated scenarios with delays and packet loss, predicted actions planned by the MPC on the edge avoided collisions between the vehicles.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> or <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

Energy-efficient proactive caching with multipath routing

The ever-continuing explosive growth of on-demand content distribution has imposed great pressure on mobile/wireless network infrastructures. To ease congestion in the network and to increase perceived user experience, caching of popular content closer to the end-users can play a significant role and as such this issue has received significant attention over the last few years. Additionally, energy saving is treated as a fundamental requirement in the design of next-generation mobile networks. However, there has been little attention to the overlapping area between energy saving and network caching especially when considering multipath routing. To this end, this paper proposes an energy-efficient caching with multipath routing support. The proposed scheme provides a joint anchoring of popular content into a set of potential caching nodes with optimized multipath support whilst ensuring a balance between transmission and caching energy cost. The proposed model also considers different content delivery modes, such as multicast and unicast . Two separated Integer-Linear Programming (ILP) models are formulated for each delivery mode. To tackle the curse of dimensionality we then provide a greedy simulated annealing algorithm , which not only reduces the time complexity but also provides a competitive performance. A wide set of numerical investigations reveal that the proposed scheme reduces the energy consumption up to 75% compared with other widely used caching approaches under the premise of network resource limitation. Sensitivity analysis to different parameters is also meticulously discussed in this paper.

SDN-Based Service Mobility Management in MEC-Enabled 5G and Beyond Vehicular Networks

The next-generation mobile cellular networks are dedicated to providing a valued and unique service experience by supporting ultrareliable and low-latency communication (URLLC), high throughput, and high availability. Multiaccess edge computing (MEC) is an emerging network solution that provides services and computing functions on edge nodes to provide users with a reliable and high-quality service experience. However, achieving satisfactory Quality of Service (QoS) for diverse service requests in a mobile environment is challenging because of the densely deployed yet resource-constrained MEC servers. A solution to ensure continued service quality is to migrate the services according to the mobility of users. However, in a highly mobile environment such as vehicular communications, this may result in a repeated relocation of services, incurring high operational costs and poor utilization of network resources. Moreover, each service has its own set of communication requirements, such as delay and bandwidth. Meeting these requirements in a highly dynamic and complex vehicular environment is an exacting challenge. Software-defined networking (SDN) concepts are leveraged in MEC to provide a unified control plane interface that performs effective network and service mobility management, to manage the heterogeneity of service requests within the resource-constrained MEC servers. We conducted various Proof-of-Concept (PoC) experiments in an overlapped vehicle-to-everything (V2X) networking environment to demonstrate the feasibility of our proposed system that ensures interconnection and federation among distributed MEC servers and mobile networks.

Intelligent-Driven Green Resource Allocation for Industrial Internet of Things In 5g Heterogeneous Network

Abstract: Mobile networks' energy consumption is rising in tandem with the volume of traffic and the number of people utilising mobile technology. To maintain the long-term survival of the next generation of mobile networks, there must be an emphasis on energy efficiency. By minimising the network's power consumption and proposing an energy-efficient network architecture, this thesis addresses the issue of increasing 5G and beyond network efficiency. The first component of this thesis focuses on base stations (BSs), the most energy-intensive part of mobile networks. Mobile network providers offer us with a data set that contains information on the amount of traffic on their system. The poor temporal granularity of mobile network traffic data makes it difficult to train ML systems for sleep mode management choices. Bursty arrivals are taken into consideration while generating mobile network traffic statistics.

Personal Health Metrics Data Management Using Symmetric 5G Data Channels

The integrated collection of personal health data represents a relevant research topic, which is enhanced further by the development of next-generation mobile networks that can be used in order to transport the acquired medical data. The gathering of personal health data has become recently feasible using relevant wearable personal devices. Nevertheless, these devices do not possess sufficient computational power, and do not offer proper local data storage capabilities. This paper presents an integrated personal health metrics data management system, which considers a virtualized symmetric 5G data transportation system. The personal health data are acquired using a client application component, which is normally deployed on the user’s mobile device, regardless it is a smartphone, smartwatch, or another kind of personal mobile device. The collected data are securely transported to the cloud data processing components, using a virtualized 5G infrastructure and homomorphically encrypted data packages. The system has been comprehensively assessed through the consideration of a real-world use case, which is presented.