Alternatives for mobile operators in the competitive 3G and beyond business

Harno, Jarmo; Katsianis, Dimitris; Smura, Timo; Eskedal, Thor Gunnar; Venturin, Rima; Pohjola, O.-P.; Kumar, K.R.R.; Varoutas, Dimitris

doi:10.1007/s11235-009-9150-4

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Whereas Harmantzis et al (2006) addressed critical concerns about how, when, and why telecoms businesses should make new investments, Harno et al (2009) proposed business alternatives to guide the investment policies of telecoms operators to alleviate these concerns. Also, Visan et al (2020) exemplified the use of methodological practices and practical results, which can reveal new business models by analyzing the data available by telecoms companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches

Şahin¹

2023

Intell Sys Acc Fin Mgmt

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Investment in the right location ensures sustainable competition. In the telecommunication sector, the number of base stations (BSs) is one of the most significant investment parameters. When a potential BS is subject to be selected, practitioners will first consider investing in a BS where the return on investment (ROI) is highest.Therefore, the quantifiable objectives are distinctly defined, as it makes sense to choose maximizing features that raise per unit investment. This study provides a solution to evaluate the best BS installation alternative with machine-learning approaches as well as to estimate ROI value by changing the properties that affect the ROI value. For this purpose, the estimation performance of logistic regression, random forest, and XGBoost methods are compared and further strengthened by random forest hyperparameter optimization to provide the best performance. The model, with a success rate of 98.7% according to the F-score, showed that it was a robust algorithm. The three most essential features for the ROI value are determined to be voice traffic, data traffic, and frequency cost. These parameters enable a review of the prediction results of telecommunications managers and planning specialists responsible for BS investment.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches

Şahin¹

2023

Intell Sys Acc Fin Mgmt

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“…However, due to the fast development of new access solutions and the uncertain conditions of rural broadband markets, pure technical superiority is not a guarantee for market success with regard to new technologies [40]. The importance of analysing different business models and technologies in the telecommunications industry is crucial for the investment policy of telecom operators [41]. As noted in [42], various social, economic, regulatory, and political issues have an important role in network rollout and broadband service offerings.…”

Section: Technical and Economic Aspects Of Broadband Implementationmentioning

confidence: 99%

A framework for optimal techno-economic assessment of broadband access solutions and digital inclusion of rural population in global information society

Križanović

Žagar

Grgić

2017

Univ Access Inf Soc

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Broadband solutions, i.e., broadband technologies and services, offer users numerous benefits. However, inequalities related to the levels of broadband solutions' deployment and adoption between rural and urban areas, i.e., the digital divide, remain a consequential problem worldwide. To reduce the digital divide, the issues related to the deployment and usage of broadband solutions in rural areas are analysed in this paper. To define optimal strategies for the deployment and adoption of rural broadband solutions, the application of an extended technoeconomic assessment process is considered. Therefore, an additional framework that extends the structure of standard techno-economic models is proposed, and its detailed overview is given. Within the framework, the regression analyses of factors relevant to broadband solutions deployment and adoption in rural areas are defined. The results of conducted analyses are used in the standard techno-economic modelling process. Finally, the efficiency of framework usage in techno-economic assessment process and in finding optimal broadband solutions is demonstrated for a case study rural scenario.

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“…In , techno‐economic modelling has been used to analyse the position of virtual network operators in the mobile communications industry. In , a techno‐economic approach has been used to identify business alternatives and opportunities for mobile operators. Techno‐economic modelling is also frequently used by national regulatory authorities for the evaluation of price setting or policy options in regulated markets .…”

Section: Techno‐economic Analysis Via Cost Modellingmentioning

confidence: 99%

How can a mobile service provider reduce costs with software‐defined networking?

et al. 2015

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Network architecture innovation has been driven by virtualization and centralization of network control based on software-defined networking (SDN) and by network functions moved to the cloud with network function virtualization (NFV). These two principles are considered as promising enablers to reduce costs and spur innovation. In the first part of the paper we argue that the evolved packet system (EPS) can be seen as a service function chain and an area in which SPs can capitalize on SDN and NFV capabilities. A multi-layer modular architecture for carrier networks based on SDN-and NFV principles is provided. In the second part of the paper we focus solely on SDN, we argue that service providers (SPs) can benefit from SDN to reach cost savings in their mobile network. Our work focuses on the cost savings that can be reached via the centralization of control and the operational paradigm of developing and operating the network management software in-house instead of buying a vendor solution only. We quantify the potential cost savings that can be reached in an Internet protocol/multi-protocol label switching (IP/MPLS)-based transport service with SDN capabilities that interconnects the key functional elements in a mobile network. In the analysis, we evaluate the impact on capital expenditures (CapEx) and provide details on the impact on operational expenditures (OpEx). The evaluation can serve as a blueprint for techno-economic analysis of the mobile network operator's processes in the transport network of a mobile network

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Alternatives for mobile operators in the competitive 3G and beyond business

Cited by 12 publications

References 8 publications

Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches

Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches

A framework for optimal techno-economic assessment of broadband access solutions and digital inclusion of rural population in global information society

How can a mobile service provider reduce costs with software‐defined networking?

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