Integration of a Cloud-Based Realistic and Automatic Coverage Estimation Methodology in Metric SaaS

Fernandes, Daniel; Soares, Gabriela; Clemente, Diogo; Cortesao, Rodrigo; Sebastião, Pedro; Cercas, Francisco; Dinis, Rui; Ferreira, Lucio Studer

doi:10.1109/wpmc48795.2019.9096207

Cited by 2 publications

(4 citation statements)

References 4 publications

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Section: Introductionmentioning

confidence: 69%

“…This work extends the ideas presented in [8]- [10], where the cell reach value and the handover distance were considered in the calibration of the model. These authors, namely in [9] and [10], include the terrain morphology but they omit the possibility of a Line of Sight (LoS) between the Mobile Terminal (MT) and the Base Station (BS). Despite the cloud implementation, using Amazon Web Services (AWS), has already been used in [10], this proposed work presents further enhancements that generalise and increase its precision, mostly related with fine-tuning of our propagation model.…”

Section: Introductionmentioning

confidence: 69%

“…In this section, a semi-empirical propagation model that accurately represents reality is detailed. It details and largely extends initial ideas drafted over [8]- [10].…”

Section: Modules Of the Automatic Coverage Estimation Frameworkmentioning

confidence: 95%

“…These authors, namely in [9] and [10], include the terrain morphology but they omit the possibility of a Line of Sight (LoS) between the Mobile Terminal (MT) and the Base Station (BS). Despite the cloud implementation, using Amazon Web Services (AWS), has already been used in [10], this proposed work presents further enhancements that generalise and increase its precision, mostly related with fine-tuning of our propagation model. Several aspects, such as the reuse of previous DT measurements and the application of LoS between the Mobile Terminal (MT) and the Base Station (BS) were also considered.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

Fernandes

Clemente

Soares³

et al. 2020

IEEE Access

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One of the main concerns of telecommunications operators is related to network coverage. A weak coverage can lead to a performance decrease, not only in the user experience, when using the operators' services, such as multimedia streaming, but also in the overall Quality of Service. This paper presents a novel cloud-based framework of a semi-empirical propagation model that estimates the coverage in a precise way. The novelty of this model is that it is automatically calibrated by using drive test measurements, terrain morphology, buildings in the area, configurations of the network itself and key performance indicators, automatically extracted from the operator's network. Requirements and use cases are presented as motivations for this methodology. The results achieve an accuracy of about 5 dB, allowing operators to obtain accurate neighbour lists, optimise network planning and automate certain actions on the network by enabling the Self-Organising Network concept. The cloud implementation enables a fast and easy integration with other network management and monitoring tools, such as the Metric platform, optimising operators' resource usage recurring to elastic resources on-demand when needed. This implementation was integrated into the Metric platform, which is currently available to be used by several operators.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 69%

“…In this section, a semi-empirical propagation model that accurately represents reality is detailed. It details and largely extends initial ideas drafted over [8]- [10].…”

Section: Modules Of the Automatic Coverage Estimation Frameworkmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

Fernandes

Clemente

Soares³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

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Cloud-Based Implementation of a SON Radio Resources Planning System for Mobile Networks and Integration in SaaS Metric

et al. 2021

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In mobile network deployments of growing size, the optimum and fast planning of radio resources are a key task. Cloud services enable efficient and scalable implementation of procedures and algorithms. In this paper, a proof of concept implementation of a cloud-based network planning work pattern using Amazon Web Services (AWS) is presented, containing new and efficient radio resource plannning algorithms for 3G, 4G and 5G systems. It extracts configuration and performance data from the network, enabling to accurately estimate cells coverage, identify neighbouring cells and optimally plan scrambling codes (SCs) and physical cell identity (PCI) in 3G and 4G/5G networks, respectively. This implementation was integrated and is available in the commercial Metric Software-as-a-Service (SaaS) monitoring and planning tool. The cloud-based planning system is demonstrated in various canonical and realistic Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) scenarios, and compared to an algorithm previously used by Metric. For a small LTE realistic scenario consisting of 9 sites and 23 cells, it takes less than 0.6 seconds to perform the planning. For an UMTS realistic scenario with 12 484 unplanned cells, the planning is efficiently achieved, taking less than 8 seconds, and guaranteeing no collisions between first order neighbouring cells. The proposed concept is proved, as this system, capable of automatically planning 3/4/5G realistic networks of multi-vendor equipment, makes Metric more attractive to the market.

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Integration of a Cloud-Based Realistic and Automatic Coverage Estimation Methodology in Metric SaaS

Cited by 2 publications

References 4 publications

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

Cloud-Based Implementation of a SON Radio Resources Planning System for Mobile Networks and Integration in SaaS Metric

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