Introduction of 5G as a Next-generation Mobile Network

5G aren't just about significantly improving network connectivity. It's a next-generation mobile network that promises to be a game changer in the way we live. The true breakthrough of 5G is the capacity of up to 1,000 5G connected devices per person. It covers all 7 billion people worldwide. One of the great expectations for the future is that not only will all humans be connected to the Internet, but most items of our lives will also be connected. With 5G, coverage will be improved, capacity will be increased, latency will be reduced, and data speed will significantly improve. Future 5G solutions will outperform current 4G mobile networks in several ways. Significant improvements in device density, transfer speeds and latencies, and a 90% reduction in power consumption are just a few of the 5G goals. On the other hand, the harmful effects of frequency radiation have already been proven. Even before 5G was proposed, dozens of petitions and appeals by international scientists, including the Flyberger appeal signed by more than 3,000 doctors, stopped the expansion of wireless technology and made new base stations. Requested a moratorium. Negative microbiological effects have also been recorded. Government regulators will consider deploying 5G, especially with the additional infrastructure needed to expand their networks. 5G deployments need to address both standard and advanced cybersecurity threats. It is the responsibility of the carrier and network consortium to provide customers with digital safety nets, except that customer complacency can be an issue as well.

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Evolution of QoS Control in Next Generation Mobile Networks

Advances in Wireless Technologies and Telecommunication - Quality of Service Architectures for Wireless Networks ◽

10.4018/978-1-61520-680-3.ch026 ◽

2010 ◽

pp. 595-612

Author(s):

Alberto Díez Albaladejo ◽

Fabricio Gouveia ◽

Marius Corici ◽

Thomas Magedanz

Keyword(s):

Wireless Networks ◽

Mobile Networks ◽

Mobile Network ◽

Next Generation ◽

Qos Control ◽

Main Research ◽

Next Generation Mobile Networks ◽

Major Trend ◽

Promising Solution ◽

Common Architecture

Next Generation Mobile Networks (NGMNs) constitute the evolution of mobile network architectures towards a common IP based network. One of the main research topics in wireless networks architectures is QoS control and provisioning. Different approaches to this issue have been described. The introduction of the NGMNs is a major trend in telecommunications, but the heterogeneity of wireless accesses increases the challenges and complicates the design of QoS control and provisioning. This chapter provides an overview of the standard architectures for QoS control in Wireless networks (e.g. UMTS, WiFi, WiMAX, CDMA2000), as well as, the issues on this all-IP environment. It provides the state-of-the-art and the latest trends for converging networks to a common architecture. It also describes the challenges that appear in the design and deployment of QoS architectures for heterogeneous accesses and the available solutions. The Evolved Core from 3GPP is analyzed and described as a suitable and promising solution addressing these challenges.

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Handover with Buffering for Distributed Mobility Management in Software Defined Mobile Networks

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/ajtde.v6n1.137 ◽

2018 ◽

Vol 6 (1) ◽

pp. 26-40

Author(s):

Battulga Davaasambuu

Keyword(s):

Mobile Networks ◽

Mobility Management ◽

Single Point ◽

Mobile Network ◽

Base Stations ◽

Data Traffic ◽

Distributed Mobility Management ◽

Mobile Cellular ◽

Network Technologies

The rapidly-growing number of mobile subscribers has led to the creation of a large number of signalling messages. This makes it difficult to efficiently handle the mobility of subscribers in mobile cellular networks. The long-term evolution (LTE) architecture provides software-defined networking (SDN) to meet the requirements of 5G networks and to forward massive mobile data traffic. The SDN solution proposes separation of the control and data planes of a network. Centralized mobility management (CMM) is widely used in current mobile network technologies, such as 4G networks. One of the problems related to CMM is a single point of failure. To solve the problems of CMM and in order to provide for efficient mobility management, IETF has developed a solution called distributed mobility management (DMM), in which mobility is handled via the nearest mobility anchor. In this paper, we propose a DMM solution with handover operations for SDN-enabled mobile networks. The advantage of the proposed solution is that intra and inter handover procedures are defined with the data buffering and forwarding processes between base stations and mobility anchors. We adopt a simulation model to evaluate and compare the proposed solution with the existing solution in terms of handover latency, packet loss and handover failures.

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Base Station Energy Efficiency Improvement for Next Generation Mobile Networks

International Journal of Electronics and Telecommunications ◽

10.1515/eletel-2017-0025 ◽

2017 ◽

Vol 63 (2) ◽

pp. 187-194 ◽

Cited By ~ 4

Author(s):

Weston Mwashita ◽

Marcel Ohanga Odhiambo

Keyword(s):

Mobile Networks ◽

Energy Efficient ◽

Carbon Dioxide Emissions ◽

Mobile Network ◽

Base Station ◽

Base Stations ◽

Data Traffic ◽

Energy Efficiency Improvement ◽

Next Generation Mobile Networks ◽

Operational Expenditure

Abstract As more and more Base Stations (BSs) are being deployed by mobile operators to meet the ever increasing data traffic, solutions have to be found to try and reduce BS energy consumption to make the BSs more energy efficient and to reduce the mobile networks’ operational expenditure (OPEX) and carbon dioxide emissions. In this paper, a BS sleeping technology deployable in heterogeneous networks (HetNets) is proposed. The proposed scheme is validated by using extensive OMNeT++/SimuLTE simulations. From the simulations, it is shown that some lightly loaded micro BSs can be put to sleep in a HetNet when the network traffic is very low without compromising the QoS of the mobile network.

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Estimation of Electromagnetic Compatibility Between DVB-T/DVB-T2 and 4G/5G in the 700 MHz Band for Co-Channel Case

Latvian Journal of Physics and Technical Sciences ◽

10.2478/lpts-2020-0025 ◽

2020 ◽

Vol 57 (5) ◽

pp. 30-38

Author(s):

G. Ancans ◽

E. Stankevicius ◽

V. Bobrovs ◽

G. Ivanovs

Keyword(s):

Monte Carlo ◽

Mobile Networks ◽

Electromagnetic Compatibility ◽

Mobile Radiocommunication ◽

Mobile Network ◽

Separation Distance ◽

Propagation Model ◽

Base Stations ◽

Mobile Service ◽

The World

AbstractThe 694–790 MHz band (700 MHz) known also as the second digital dividend was allocated to the mobile radiocommunication service on a primary basis in Region 1 and identified to International Mobile Telecommunications by the World Radiocommunication Conference 2012 (WRC-12). The designation of mobile service in Europe and other countries of Region 1 in 700 MHz band was obtained after the World Radiocommunication Conference 2015 (WRC-15). Administrations of Region 1 will be able to plan and use these frequencies for mobile networks, including IMT. The goal of this study is to estimate the electromagnetic compatibility of Digital Video Broadcasting – Terrestrial (DVB-T/DVB-T2) and LTE (Long Term Evolution) technology operating both in 700 MHz band. The study assumes frequency division duplex (FDD) channel arrangement of 703–733 MHz (for uplink) and of 758–788 MHz (for downlink).The model contains two parts: a DVB-T/DVB-T2 system and LTE mobile broadband network. Co-channel scenario is considered in this paper, and possible impact of DVB-T/DVB-T2 on LTE base stations (receivers) is also investigated. The Monte Carlo simulations within SEAMCAT software and the Minimum Coupling Loss (MCL) method are used for interference investigation. The coordination trigger field strength value predetermined by GE06 Agreement is also used in this study. The Monte Carlo method presents more relaxed electromagnetic compatibility scenario in comparison with the MCL method. For SEAMCAT simulations, ITU-R P.1546-5 radio propagation model is used.The obtained results present the required minimum separation distance between DVB-T/DVB-T2 and LTE networks in the 694–790 MHz in order to provide the necessary performance of LTE mobile network.

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How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

Sensors ◽

10.3390/s22010255 ◽

2021 ◽

Vol 22 (1) ◽

pp. 255

Author(s):

Josip Lorincz ◽

Zonimir Klarin

Keyword(s):

Energy Efficiency ◽

Power Consumption ◽

Mobile Networks ◽

Access Network ◽

Mobile Network ◽

Network Capacity ◽

Base Stations ◽

Density Class ◽

Radio Access ◽

The Impact

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

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Mobile Network Forensics

Mobile Network Forensics - Advances in Digital Crime, Forensics, and Cyber Terrorism ◽

10.4018/978-1-5225-5855-2.ch008 ◽

2018 ◽

pp. 287-308

Keyword(s):

Mobile Networks ◽

Network Architecture ◽

Mobile Network ◽

Next Generation ◽

Network Forensics ◽

Implementation Challenges ◽

Mobile Forensics ◽

Fifth Generation ◽

Highly Effective ◽

Effective Discipline

Mobile networks are evolving towards the fifth generation, with radical changes in the delivery of user services. To take advantage of the new investigative opportunities, mobile network forensics need to address several technical, legal, and implementation challenges. The future mobile forensics need to adapt to the novelties in the network architecture, establish capabilities for investigation of transnational crimes, and combat clever anti-forensics methods. At the same time, legislation needs to create an investigative environment where strong privacy safeguards exist for all subjects of investigation. These are rather complex challenges, which, if addressed adequately, will ensure investigative continuity and keep the reputation of mobile network forensics as a highly effective discipline. In this context, this chapter elaborates the next-generation of mobile network forensics.

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Handover with Buffering for Distributed Mobility Management in Software Defined Mobile Networks

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v6n1.137 ◽

2018 ◽

Vol 6 (1) ◽

pp. 26-40

Author(s):

Battulga Davaasambuu

Keyword(s):

Mobile Networks ◽

Mobility Management ◽

Single Point ◽

Mobile Network ◽

Base Stations ◽

Data Traffic ◽

Distributed Mobility Management ◽

Mobile Cellular ◽

Network Technologies

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Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Entropy ◽

10.3390/e21111077 ◽

2019 ◽

Vol 21 (11) ◽

pp. 1077

Author(s):

Denis Horvath ◽

Juraj Gazda ◽

Eugen Slapak ◽

Taras Maksymyuk

Keyword(s):

Mobile Networks ◽

Mobile Network ◽

Cost Effective ◽

Base Stations ◽

Target Area ◽

Modeling And Analysis ◽

Network Failure ◽

On Demand ◽

Wide Range ◽

Post Disaster

Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations.

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A Game Theoretic Approach to Guarantee Fairness in Cooperation Among Green Mobile Network Operators

International Journal of Business Data Communications and Networking ◽

10.4018/jbdcn.2013070101 ◽

2013 ◽

Vol 9 (3) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Leonardo Militano ◽

Antonella Molinaro ◽

Antonio Iera ◽

Ármin Petkovics

Keyword(s):

Mobile Networks ◽

Optimal Algorithm ◽

Mobile Network ◽

Base Stations ◽

Theoretic Approach ◽

Game Theoretic ◽

Cellular Mobile ◽

Mobile Network Operators ◽

Rational Players ◽

Game Theoretic Approach

Energy efficiency is one of leading design principles for the current deployment of cellular mobile networks. A first driving reason for this is that half of the operating costs for the network providers comes from the energy spent to power the network, with almost 80% of it being consumed at the base stations. A second reason is related to the high environmental pollution, which makes the green cellular networks deployment mandatory. Cooperation between mobile network providers can be an effective way to reduce the CO2 emissions and, simultaneously, reduce the operating expenditures. In this paper, a game theoretic approach is proposed to introduce fairness and stability into an optimal algorithm for switching off the cooperating base stations. This aims at making such a solution more attractive in real implementation scenarios where profit-driven network providers act as rational players.

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A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9040053 ◽

2020 ◽

Vol 9 (4) ◽

pp. 53

Author(s):

Basma Mahdy ◽

Hazem Abbas ◽

Hossam Hassanein ◽

Aboelmagd Noureldin ◽

Hatem Abou-zeid

Keyword(s):

Time Series ◽

Mobile Networks ◽

Time Series Data ◽

Mobile Network ◽

Base Station ◽

Optimization Techniques ◽

Base Stations ◽

Traffic Load ◽

Series Data ◽

Better Than

Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques.

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