On Optimal Infrastructure Sharing Strategies in Mobile Radio Networks

Cano, Lorela; Capone, Antonio; Carello, Giuliana; Cesana, Matteo; Passacantando, Mauro

doi:10.1109/twc.2017.2673841

Cited by 27 publications

(16 citation statements)

References 20 publications

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“…Thus, in order to reduce the per-MN operator investment cost, the sharing of network infrastructure between mobile operators is an attractive solution. To this effect, the authors in [12] proposed a RAN sharing scheme where MN operators share a single radio infrastructure while maintaining separation and full control over the backhauling and their respective core networks. In that paper, a mixed-integer linear programming (MILP) formulation is proposed for determining the sharing configurations that maximize the QoS, and a cooperative game theory concept is used to determine stable configurations as envisioned by the MN operator.…”

Section: Related Workmentioning

confidence: 99%

“…Again, here, the other party has to pay in order to use those facilities. However, it is worth mentioning that the works done in [12][13][14][15] do not consider infrastructure sharing with the MEC paradigm and the consideration of green energy has been overlooked. Those that are within 3 Wireless Communications and Mobile Computing the MEC paradigm share their own network resources, among themselves in order to handle spatially uneven computation workloads in the network.…”

Section: Related Workmentioning

confidence: 99%

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Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

Dlamini

Vilakati

2021

Wireless Communications and Mobile Computing

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As mobile networks (MNs) are advancing towards meeting mobile user requirements, the rural-urban divide still remains a major challenge. While areas within the urban space (metropolitan mobile space) are being developed, i.e., small Base Stations (BSs) empowered with computing capabilities are deployed to improve the delivery of user requirements, rural areas are left behind. Due to challenges of low population density, low income, difficult terrain, nonexistent infrastructure, and lack of power grid, remote areas have low digital penetration. This situation makes remote areas less attractive towards investments and to operate connectivity networks, thus failing to achieve universal access to the Internet. In addressing this issue, this paper proposes a new BS deployment and resource management method for remote and rural areas. Here, two MN operators share their resources towards the procurement and deployment of green energy-powered BSs equipped with computing capabilities. Then, the network infrastructure is shared between the mobile operators, with the main goal of enabling energy-efficient infrastructure sharing, i.e., BS and its colocated computing platform. Using this resource management strategy in rural communication sites guarantees a quality of service (QoS) comparable to that of urban communication sites. The performance evaluation conducted through simulations validates our analysis as the prediction variations observed show greater accuracy between the harvested energy and the traffic load. Also, the energy savings decrease as the number of mobile users (50 users in our case) connected to the remote site increases. Lastly, the proposed algorithm achieves 51% energy savings when compared with the 43% obtained by our benchmark algorithm. The proposed method demonstrates superior performance over the benchmark algorithm as it uses foresighted optimization where the harvested energy and the expected load are predicted over a given short-term horizon.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

Dlamini

Vilakati

2021

Wireless Communications and Mobile Computing

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“…Unlike in [108,109,127], in [25,26] we (i) account for both the technical and economic aspects of sharing reflected in the payoff function definition and (ii) do not split the shared infrastructure cost among MNOs a priori; how these cost are split is an outcome of the model (game). In turn in [27], we address a similar scenario to [25,26] but without spectrum pooling. Moreover, in [27] we consider two different cases deriving from two different perspectives, the one of a regulatory entity favoring the users and the MNOs' perspective as profit-maximizers.…”

Section: Infrastructure Sharing Among Conventional Mnosmentioning

confidence: 99%

“…In turn in [27], we address a similar scenario to [25,26] but without spectrum pooling. Moreover, in [27] we consider two different cases deriving from two different perspectives, the one of a regulatory entity favoring the users and the MNOs' perspective as profit-maximizers. We model the former case through Mixed Integer Linear Programming and the latter through cooperative game theory.…”

Section: Infrastructure Sharing Among Conventional Mnosmentioning

confidence: 99%

On the evolution of infrastructure sharing in mobile networks: A survey

Sansò¹,

Cano²,

Capone³

2020

ITU J-FET

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Infrastructure sharing for mobile networks has been a prolific research topic for more than three decades now. The key driver for Mobile Network Operators to share their network infrastructure is cost reduction. Spectrum sharing is often studied alongside infrastructure sharing although on its own it is a vast research topic outside the scope of this survey. Instead, in this survey we aim to provide a complete picture of infrastructure sharing both over time and in terms of research branches that have stemmed from it such as performance evaluation, resource management etc. We also put an emphasis on the relation between infrastructure sharing and the decoupling of infrastructure from services, wireless network virtualization and multi-tenancy in 5G networks. Such a relation reflects the evolution of infrastructure sharing over time and how it has become a commercial reality in the context of 5G.

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“…Prone to high inaccuracy due to method. Weighted Centroid [2,14,17] Classify each measurement to a weight of 0 and 1 using formula below: [10,20,24,36] Strongest RSS estimates a cell's location as the location of the measurement with the strongest observed RSS from that cell. This approach works well when a cell is located close to the road where wardriving measurements were collected, but often fails otherwise.…”

Section: Algorithm Approach Disadvantagesmentioning

confidence: 99%

A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

2019

IJRTE

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The vast amount of mobile smartphone users provides an infinite source of data for crowdsourcing. Crowdsourcing provides an economical method of gathering data to cover a large geographical area compared to traditional methods. However, the inaccurate predictions for base station localization derived from mobile crowdsourcing impacts its effectiveness for use in radio planning. Therefore, the purpose of this study is to design a model that can yield a more accurate localization through the introduction of a rule-based weighted classification. The methodology deployed is a permutation series based on fingerprint of the cell site with weightage derived from rule-based classification. DeLaunay triangulation and Voronoi diagrams are used to determine the positions of the existing base stations and the prediction of new site location respectively. The expected results are better accuracy of the classification model in the localization prediction of the base station leading to a more accurate prediction of new site location.

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On Optimal Infrastructure Sharing Strategies in Mobile Radio Networks

Abstract: Abstract-

Cited by 27 publications

References 20 publications

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

On the evolution of infrastructure sharing in mobile networks: A survey

A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

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