scholarly journals Developing a Framework for Future Mobile Data Pricing

2017 ◽  
Vol 21 (2) ◽  
pp. 84 ◽  
Author(s):  
Michael Paetsch ◽  
Peter Dorčák ◽  
František Pollák ◽  
Ľubomír Štrba ◽  
Branislav Kršák
Keyword(s):  
Mobile Networks ◽  
Economic Value ◽  
Network Capacity ◽  
Data Traffic ◽  
Fixed Amount ◽  
Mobile Data ◽  
Data Value ◽  
Voice Calls ◽  
The Impact ◽  
New Framework

<p><strong>Purpose:</strong> The revenues for mobile data transmission overtook the revenue of voice calls for the first time in 2014 in the USA. It can be observed that demand for mobile data – largely driven by video and cloud - is increasing exponentially, while overall data revenue is rising only moderately. This will lead to insufficient revenues stream to increase investments into mobile networks and ensure quality service. Consequently, hereof network performance will deteriorate sharply. At the heart of the problem is the current global pricing regime of fixed multiple MB/GB bundles, irrespective of time of the day, intensity of usage (e.g. video vs. email) and underlying economic value of the data. A new framework is proposed as to optimize and align network capacity and implicit data value/utility, which is crucial to ensure customer satisfaction and access justice.</p><p><strong>Methodology/Approach:</strong> The fundamental differences in pricing voice and data in voice and/or data centric networks are analysed in detail. Information has been synthesized as to develop insights into the impact of different devises and type of digital traffic for the overall performance of mobile networks. Based hereupon, a new framework for mobile data has been proposed to address the increasing misalignment between network capacity, usage and underlying data value/utility. Initial solutions have been proposed and discussed.</p><p><strong>Findings:</strong> While voice calls are easily quantifiable and are largely predictable in its occurrence and network load implications, mobile data traffic shows very large variations depending on type of traffic. While social media messaging by many customers consumes very little capacity, consumption of video streaming by relatively few customers can lead already to network saturation.</p><p><strong>Research Limitation/implication:</strong> Carriers set prices for a fixed amount of data – irrespective of intensity and time of data traffic - which leads to sharp spiky type of traffic patterns essentially signalling sharp overuse during busy hours coexist with large period of underused times.</p><strong>Originality/Value of paper:</strong> A new framework for proposition building and particularly pricing of mobile data services is provided.

Measuring the impact of redirecting and offloading mobile data traffic

2013 ◽  
Vol 18 (1) ◽  
pp. 81-103 ◽  
Author(s):  
Robert A. Novo ◽  
Christopher J. Davolos ◽  
Z. John Zhao
Keyword(s):  
Data Traffic ◽  
Mobile Data ◽  
The Impact

Mobile Data Offloading for Streaming-Class Traffic with QoS Guarantee

2015 ◽  
Vol 7 (4) ◽  
pp. 26-42
Author(s):  
Anusree Ajith ◽  
T. G. Venkatesh
Keyword(s):  
Mobile Network ◽  
Network Capacity ◽  
Live Streaming ◽  
Data Traffic ◽  
Mobile Data ◽  
Qos Guarantee ◽  
Real Time Traffic ◽  
Video And Audio ◽  
Mobile Network Operators ◽  
Mobile Data Offloading

Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading data traffic onto Wi-Fi in order to avoid congestion in the backbone is an important step in the evolution of HetNets. On-the-spot and delayed offloading have been widely studied in the literature. This paper proposes an offloading algorithm which has low computational complexity. The proposed algorithm offloads data based on a balking function which is dependent on present network condition. Using extensive simulations, the authors demonstrate that the proposed algorithm achieves reduction in mean transmission delay without sacrificing much on the offloading efficiency. This technique is more efficient and applicable to real-time traffic, like live streaming video and audio, which has short and stringent delay requirements or deadlines.

scholarly journals SDN-Based Mobile Data Offloading Scheme Using a Femtocell and WiFi Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Chang-Woo Ahn ◽  
Sang-Hwa Chung
Keyword(s):  
Target Selection ◽  
Network Capacity ◽  
Centralized Control ◽  
Data Traffic ◽  
Wifi Networks ◽  
Selection Scheme ◽  
Mobile Data ◽  
Available Bandwidth ◽  
Seamless Connectivity ◽  
The Many

Because of the many applications running on smartphones, the load of mobile data traffic on cellular networks is increasing rapidly. A femtocell is a solution to increase the cellular network capacity and coverage. However, because it uses the same frequency bands as a macrocell, interference problems have prevented its widespread adoption. In this paper, we propose a scheme for traffic offloading between femtocells and WiFi networks utilizing software-defined networking (SDN) technology. In the proposed offloading scheme, the SDN technology allows a terminal to maintain existing sessions after offloading through a centralized control of the SDN-based equipment. We also propose an offloading target selection scheme based on available bandwidth estimation and an association control mechanism to reduce the femtocell load while ensuring quality of service (QoS) in terms of throughput. Experimental results on an actual testbed showed that the proposed offloading scheme provides seamless connectivity and reduces the femtocell load by up to 46% with the aid of the proposed target selection scheme, while ensuring QoS after offloading. We also observed that the proposed target selection scheme offloads 28% more traffic to WiFi networks compared to received signal strength indicator-based target selection in a low background traffic environment.

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.

scholarly journals Wi-Fi a simple and efficient way for offloading data traffic in mobile networks

2013 ◽  
Vol 2 (1) ◽  
pp. 16-23
Author(s):  
Irena Malolli ◽  
Kozeta Sevrani
Keyword(s):  
Video Compression ◽  
Mobile Networks ◽  
Traffic Management ◽  
Business Models ◽  
Smart Devices ◽  
Technical Solution ◽  
Data Traffic ◽  
Management Tools ◽  
Mobile Data ◽  
High Data

Mobile data traffic is significantly increased year by year due to a number of factors including new smart devices, new applications such as M2M, the so-called “always-on” applications and services etc. In addition the recent studies tell us that the forecasts for mobile data traffic in near future will be tenfold higher, while the revenue for this market is expected to be increased only twofold. This trend raised a number of challenges for the mobile network operators (MNOs) in the world and in our region. Different technical and commercial solutions are discussed and developed and / or under developing. The first idea how to cope with high data traffic is to increase the network capacities. Even this is a direct traditional way as a technical solution it is too expensive and time consuming. Alternative ways to cope with data traffic in order to satisfy consumer demand and to keep key performance indicators are under developing. Some solutions in place are linked with traffic management tools such as data optimization, throttling, filtering, caching, video compression etc. In addition, new pricing policies and the adoption of the appropriate business models in new era of mobile data traffic are in the process. On top of the ways mentioned above or alternatively, Wi-Fi is considered as a simple way of data traffic off-load in mobile networks. In this article, we will identify the positive aspects of Wi-Fi offload versus other traffic management tools and draw some conclusions. We will give some recommendations how MNOs improve the situation for high data traffic through Wi-Fi offload solution, how Wi-Fi offload is related with other commercial aspects and quality of service in order to meet the customer satisfaction.

scholarly journals Efficiency Perspective on Telecom Mobile Data Traffic

2020 ◽  
Vol 5 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Riko Hendrawan ◽  
Kristian WA Nugroho ◽  
Gayuh T Permana
Keyword(s):  
Service Providers ◽  
Negative Impact ◽  
Efficiency Score ◽  
Digital Transformation ◽  
Data Traffic ◽  
Mobile Data ◽  
Digital World ◽  
Dea Method ◽  
The Impact ◽  
Over The Top

Objective – The global industry is transforming into a digital world, evidenced by digital transformation performed by almost all of the industry sectors. One of the digital drivers is the support of connectivity provided by the telecommunication industry. The increasing mobile subscribers, along with the growth of mobile data traffic, is the sign of digital transformation itself. However, the rise of OTT (Over the Top) service providers tends to acquire the revenue share of the current telecom industry, seeing the trend of voice and SMS revenue that projected to decline. Methodology/Technique – This research is intended to measure the impact of increasing mobile data traffic that mostly caused by OTT services to telecom efficiency. The efficiency measurement & analysis were performed using the Stochastic Frontier Approach (SFA) & Data Envelopment Analysis (DEA) method. Findings – By using the SFA method, Maxis (Malaysia) got the highest efficiency score (0.98), followed by AIS (Thailand) with efficiency score 0.94 and Indosat Ooredoo (Indonesia) as the least efficient telecom provider (0.5). However, by using the DEA method, TLKM (Indonesia) got the highest efficient (0.98), and Celcom Axiata (Malaysia) was the least efficient (0.73/0.8). Novelty – The compelling results of this study are variable total asset variable had a significant negative impact on the efficiency score, and the variable of mobile data traffic was not significantly impacting the efficiency value (t-Ratio 0.71). Type of Paper: Empirical. Keywords: Telecom Operators; Efficiency; Mobile Data Traffic Reference to this paper should be made as follows: Hendrawan, R; Nugroho, K.W.A; Permana,G.T. 2019. Efficiency Perspective on Telecom Mobile Data Traffic, J. Bus. Econ. Review 5(1) 38 – 44 https://doi.org/10.35609/jber.2020.5.1(5) JEL Classification: M10, M15, M19.

scholarly journals Estimation of Spectrum Requirements for Mobile Networks With Self-Similar Traffic, Handover, and Frequency Reuse

2010 ◽  
Vol 6 (4) ◽  
pp. 281-291
Author(s):  
Won Seok Yang ◽  
Eun Saem Yang ◽  
Hwa J. Kim ◽  
Dae K. Kim
Keyword(s):  
Mobile Networks ◽  
Probability Distributions ◽  
Frequency Reuse ◽  
Estimation Methods ◽  
Data Traffic ◽  
Load Spectrum ◽  
Self Similarity ◽  
Average Cell ◽  
Self Similar ◽  
The Impact

This paper considers self-similarity in data traffic, handover, and frequency reuse to estimate the spectrum requirements of mobile networks. An approximate average cell capacity subject to a delay requirement and self-similar traffic is presented. It is shown that handover traffic can be an additional load. Spectrum requirements are calculated based on carrier demand instead of spectral efficiency, as at least one carrier is necessary to transmit even 1 bit. The cell-split operation is considered under frequency reuse of one. Estimation methods are presented using cell traffic in two cases. First, a procedure is presented that estimates cell traffic from previous networks. Second, cell traffic is assumed to follow probability distributions. Numerical examples demonstrate the impact of self-similarity, handover, and the proportion of cell-split occurrences on the spectrum requirements.

Introduction to Femtocell

2012 ◽  
pp. 1-14 ◽  
Author(s):  
Rania A. Mokhtar ◽  
Rashid Saeed ◽  
Bharat S. Chaudhari
Keyword(s):  
Mobile Networks ◽  
Cellular Network ◽  
Business Models ◽  
Low Cost ◽  
Access Point ◽  
Network Capacity ◽  
Indoor Coverage ◽  
The Cost ◽  
The Impact ◽  
Home Node

Femtocell is a licensed indoor coverage solution served by a residential licensed access point known as FAP or Home node B. Femtocell promises to address the cost and coverage issues of mobile networks and increase cellular network capacity by rising above the impact of wall attenuation on macrocell deployment. The Femto Forum defines femtocell as a low cost access point leveraged on mature mobile technology that operates on a licensed spectrum and utilizes broadband (IP) as backhaul. This chapter gives an overview of the femtocell technology and architecture, standard and business models.

scholarly journals Energy consumption of mobile data transfer – Increasing or decreasing? Evaluating the impact of technology development & user behavior

10.29007/ddrc ◽  
2018 ◽  
Author(s):  
Hanna Pihkola ◽  
Mikko Hongisto ◽  
Olli Apilo ◽  
Mika Lasanen ◽  
Saija Vatanen
Keyword(s):  
Energy Consumption ◽  
Consumer Behavior ◽  
Mobile Networks ◽  
Energy Demand ◽  
Energy Use ◽  
Data Transfer ◽  
Continuous Growth ◽  
Mobile Data ◽  
Trade Offs ◽  
The Impact

Mobile data consumption in Finland is among the highest in the world. Increase in mobile data usage has been rapid and continuous growth is foreseen. While the energy consumed per transmitted gigabyte has substantially decreased, it seems that the absolute annual energy consumption related to mobile operators’ activities has started to increase. Simultaneously, consumer behavior is changing. While new end-user devices are more and more energy-efficient, we use more and more time with mobile devices. Is increasing usage outweighing achieved energy savings? What kinds of options are available for tackling increasing energy demand?This paper discusses current and future trends related to energy consumption of mobile data transfer and mobile networks in Finland. Using a top-down approach and publicly available data, an illustrative trend (kWh/gigabyte) for the energy consumption of transmitted mobile data was constructed for the years 2010-2016. In addition, energy consumption related to mobile data transfer is discussed from a life cycle perspective, considering both direct and indirect energy use and challenges in conducting such assessments. Contributions of relevant technological and social developments (radio network technology transformations from 4G to 5G and consumer behavior) are analyzed considering possible trade-offs and pointing out aspects that require future studies.

scholarly journals A Study of the Effects on QoS in WiFi-Cellular Offloading Scenarios

2019 ◽  
Vol 4 (3) ◽  
pp. 795-802
Author(s):  
D. Srinivasa Rao ◽  
G. B. S. R. Naidu
Keyword(s):  
Cellular Network ◽  
Quality Of Experience ◽  
Network Capacity ◽  
Smart Devices ◽  
Data Traffic ◽  
Integrated Networks ◽  
Mobile Data ◽  
Download Time ◽  
Delay Performance

Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.

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