scholarly journals A Mobile Network Planning Tool Based on Data Analytics

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jessica Moysen ◽  
Lorenza Giupponi ◽  
Josep Mangues-Bafalluy
Keyword(s):  
Mobile Networks ◽  
Network Planning ◽  
Mobile Network ◽  
Cost Effective ◽  
Planning Tool ◽  
Network Nodes ◽  
Radio Access ◽  
Physical Resource Block ◽  
History Of ◽  
Multiple Challenges

Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity of layers, applications, and Radio Access Technologies (RAT). In this context, a network planning tool capable of dealing with this complexity is highly convenient. The objective is to exploit the information produced by and already available in the network to properly deploy, configure, and optimise network nodes. This work presents such a smart network planning tool that exploits Machine Learning (ML) techniques. The proposed approach is able to predict the Quality of Service (QoS) experienced by the users based on the measurement history of the network. We select Physical Resource Block (PRB) per Megabit (Mb) as our main QoS indicator to optimise, since minimizing this metric allows offering the same service to users by consuming less resources, so, being more cost-effective. Two cases of study are considered in order to evaluate the performance of the proposed scheme, one to smartly plan the small cell deployment in a dense indoor scenario and a second one to timely face a detected fault in a macrocell network.

DIDERO 3G A STRATEGIC NETWORK PLANNING TOOL FOR 3G MOBILE NETWORKS

2003 ◽  
Vol 02 (04) ◽  
pp. 531-555 ◽  
Author(s):  
KLAUS D. HACKBARTH ◽  
J. ANTONIO PORTILLA
Keyword(s):  
Strategic Planning ◽  
Mobile Networks ◽  
Input Data ◽  
Network Planning ◽  
Mobile Network ◽  
Hybrid Networks ◽  
User Mobility ◽  
Planning Tool ◽  
2Nd Generation

Strategic Planning for mobile networks has to consider the evolution from the existing 2nd Generation GSM architecture to a long term 3rd Generation UMTS architecture over various intermediate steps in the form of hybrid networks. The study of this evolution requires a corresponding Strategic Planning tool. This article deals with Strategic Network Planning and develops the corresponding models considering aspects like radio propagation, multi-service traffic and user mobility. The article presents a corresponding strategic planning tool which allows to study the evolution of a mobile network under the corresponding input data in the form of a service and topographic scenarios. The application of the tool to a specific network region is shown and costing perspectives for the different services are discussed.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

2G-4G Networks

2011 ◽  
pp. 964-973 ◽  
Author(s):  
Shakil Akhtar
Keyword(s):  
Mobile Networks ◽  
Mobile Network ◽  
Local Area ◽  
Mobile Systems ◽  
Fourth Generation ◽  
Stationary Mode ◽  
Download Time ◽  
Radio Access ◽  
4G Wireless ◽  
Wireless Mobile

The fourth-generation wireless mobile systems, commonly known as 4G, is expected to provide global roaming across different types of wireless and mobile networks; for instance, from satellite to mobile networks and to Wireless Local Area Networks (WLANs). 4G is an all IP-based mobile network using different radio access technologies and providing seamless roaming and connection via always the best available network (Zahariadis & Kazakos, 2003). The vision of 4G wireless/mobile systems will be the provision of broadband access, seamless global roaming and Internet/data/voice everywhere, utilizing for each the most “appropriate” always-best connected technology (Gustafsson & Jonsson, 2003). These systems are about integrating terminals, networks and applications to satisfy increasing user demands (Ibrahim, 2002; Lu & Berezdivin, 2002). 4G systems are expected to offer a speed of more than 100 Mbps in stationary mode and an average of 20 Mbps for mobile stations, reducing the download time of graphics and multimedia components by more than 10 times compared to currently available 2 Mbps on 3G systems.

Very High Data Rate Support for Mobile Cloud Services Using SDN-Based Heterogeneous Carrier Aggregation

2014 ◽  
Vol 24 (07) ◽  
pp. 983-1011
Author(s):  
Dong Ha Kim ◽  
Cheolhoon Kim ◽  
Yumi Oh ◽  
Sungwon Lee ◽  
Seung Gwan Lee
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Data Transfer ◽  
Cost Effective ◽  
Data Rate ◽  
Cloud Services ◽  
Carrier Aggregation ◽  
Radio Access ◽  
5G Mobile Networks

Mobile traffic is currently the most important traffic on the Internet, both domestically and internationally. This is predominantly attributable to widespread cloud computing-based services on mobile networks. Fifth generation (5G) mobile networks are expected to comfortably accommodate this type of traffic by supporting 1,000 times faster speeds than conventional 4G. However, commercial implementation of 5G mobile is targeted for the year 2020. Therefore, interim accommodation approaches are needed. In essence, 5G mobile networks are an evolution of 4G in which a licensed spectrum that generates revenue from user data transfer is being considered. Consequently, more cost-effective methods that utilize the unlicensed spectrum are also desired. Meanwhile, concomitant with pursuit of miniaturization and reduction in weight, the number and types of network interfaces in recent mobile devices are increasing. We propose a method for carrier aggregation of multiple heterogeneous wireless links based on software-defined networking (SDN) that controls traffic and manages the wireless interfaces of mobile devices effectively when multiple radio access technologies are available. Evaluations conducted of the proposed method on an experimental testbed developed using the OpenStack cloud computing platform with aggregated multiple radio access technology environments indicate that it is feasible and provides higher data rate.

scholarly journals Modified reinforcement learning based- caching system for mobile edge computing

2020 ◽  
pp. 1-16
Author(s):  
Sarra Mehamel ◽  
Samia Bouzefrane ◽  
Soumya Banarjee ◽  
Mehammed Daoui ◽  
Valentina E. Balas
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Nodes ◽  
Caching Strategy ◽  
Edge Network ◽  
The Right ◽  
And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

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

Sensors ◽  
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.

Optimal Route Selection Algorithm for Multi-Homed Mobile Network

2012 ◽  
pp. 288-321
Author(s):  
Sulata Mitra
Keyword(s):  
Mobile Networks ◽  
Mobile Network ◽  
Route Optimization ◽  
Network Node ◽  
Route Selection ◽  
Optimal Route ◽  
Network Nodes ◽  
Multiple Routes ◽  
Multiple Network ◽  
Point To Point

This chapter develops the concept of route optimization in a multi-homed mobile network. In a future wireless network a user may have multiple mobile devices, each having multiple network interfaces and needing interconnection with each other as well as with other networks to form a mobile network. Such mobile networks may be multi-homed i.e. having multiple points of attachment to the Internet. It forwards packets of mobile network nodes inside it to Internet using suitable routes. But there may be multiple routes in a mobile network for forwarding packets of mobile network node. Moreover, the mobile network nodes inside a mobile network may have packets of different service types. So the optimal route selection inside a mobile network depending upon the service type of mobile network node is an important research issue. Two different route optimization schemes to create point to point network among mobile network nodes are elaborated in this chapter. This chapter is aimed at the researchers and the policy makers making them aware of the different means of efficient route selection in a multi-homed mobile network as well as understanding the problem areas that need further vigorous research.

Extended Mobile IPv6 Route Optimization for Mobile Networks in Local and Global Mobility Domain

2012 ◽  
pp. 183-197
Author(s):  
Arun Prakash ◽  
Rajesh Verma ◽  
Rajeev Tripathi ◽  
Kshirasagar Naik
Keyword(s):  
Mobile Networks ◽  
Mobile Network ◽  
Mobile Ipv6 ◽  
Route Optimization ◽  
Area Network ◽  
Network Mobility ◽  
Network Nodes ◽  
Global Mobility ◽  
Mobile Router ◽  
Personal Area Network

Network mobility (NEMO) route optimization support is strongly demanded in next generation networks; without route optimization the mobile network (e.g., a vehicle) tunnels all traffic to its Home Agent (HA). The mobility may cause the HA to be geographically distant from the mobile network, and the tunneling causes increased delay and overhead in the network. It becomes peculiar in the event of nesting of mobile networks due to pinball routing, for example, a Personal Area Network (PAN) inside a vehicle. The authors propose an Extended Mobile IPv6 route optimization (EMIP) scheme to enhance the performance of nested mobile networks in local and global mobility domain. The EMIP scheme is based on MIPv6 route optimization and the root Mobile Router (MR) performs all the route optimization tasks on behalf of all active Mobile Network Nodes (MNNs). Thus, the network movement remains transparent to sub MRs and MNNs and modifies only MRs and MNNs leaving other entities untouched and is more efficient than the Network Mobility Basic Support protocol (NEMO BS). The authors carried out an extensive simulation study to evaluate the performance of EMIP.

Strategic Mobile Network Planning Tool for 2G/3G Regulatory Studies

2012 ◽  
pp. 291-302
Author(s):  
Juan Eulogio Sánchez-García ◽  
Amir M. Ahmadzadeh ◽  
Beatriz Saavedra-Moreno ◽  
Sancho Salcedo-Sanz ◽  
J. Antonio Portilla-Figueras
Keyword(s):  
Network Planning ◽  
Mobile Network ◽  
Planning Tool ◽  
Mobile Network Planning

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

Entropy ◽  
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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