Combining Drive Tests and Automatically Tuned Propagation Models in the Construction of Path Loss Grids

Fernandes, Daniel; Ferreira, Lucio Studer; Nozari, Mohammad; Sebastião, Pedro; Cercas, Francisco; Dinis, Rui

doi:10.1109/pimrc.2018.8580708

Cited by 9 publications

(8 citation statements)

References 1 publication

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“…In summary, in contrast to present study, the effect of bin width and positioning error on the spatial interpolation techniques investigated in [25]- [37], remains unexplored.…”

Section: ) Data Sparsity Onlycontrasting

confidence: 59%

“…The problem of sparsity of UE reports is investigated in [25]- [37]. Authors in [25] use regression clustering for construction of received signal strength maps from a sparse set of MDT measurements.…”

Section: ) Data Sparsity Onlymentioning

confidence: 99%

“…Instead, it considers the interpolation performance for an average location error of 18 meters only. Another methodology to build complete path loss grids for a given site from sparse user measurements is proposed in [37], where the path loss is estimated for missing locations by tuning a propagation model and extrapolating the path loss for neighboring pixels, using the available drive test measurements for certain pixels. For each of these pixels, a parameter from the standard propagation model (SPM) is tuned so that the resulting path loss equals the measured value.…”

Section: ) Data Sparsity Onlymentioning

confidence: 99%

“…subject to w min  w  w max (37) positioning error radius = u (38) Owing to the small search space, we can solve (36)-(38) via brute force.…”

Section: B Determining Optimal Bin Widthmentioning

confidence: 99%

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Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging measurement reports gathered at the user equipment (UE) without the need for drive tests. Therefore, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in future cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. We characterize three key types of errors in MDT-based coverage estimation that stem from inaccurate user positioning, scarcity of user reports and quantization. For the first time, the presented analysis shows existence of joint interplay between these errors on coverage estimation that result from inter-dependency between positioning error and bin width. The analysis also shows that there exists an optimal bin width for given user positioning inaccuracy and user density that minimizes the overall error in MDT-based estimated coverage. Utility of our framework is presented by addressing two applications from network optimization perspective: determining optimal bin width to maximize accuracy of MDT-based coverage estimation and its calibration to further improve its accuracy. INDEX TERMS Minimization of drive test, autonomous coverage estimation, optimal bin width, sparse data, coverage calibration, data driven optimization

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“…In summary, in contrast to present study, the effect of bin width and positioning error on the spatial interpolation techniques investigated in [25]- [37], remains unexplored.…”

Section: ) Data Sparsity Onlycontrasting

confidence: 59%

Section: ) Data Sparsity Onlymentioning

confidence: 99%

Section: ) Data Sparsity Onlymentioning

confidence: 99%

“…subject to w min  w  w max (37) positioning error radius = u (38) Owing to the small search space, we can solve (36)-(38) via brute force.…”

Section: B Determining Optimal Bin Widthmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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“…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).…”

Section: Introductionmentioning

confidence: 69%

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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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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Combining Drive Tests and Automatically Tuned Propagation Models in the Construction of Path Loss Grids

Cited by 9 publications

References 1 publication

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

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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