Browsing by Author "Duarte, David"
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- An enhanced capacity model based on network measurements for a multi-service 3G systemPublication . Parracho, Diogo; Duarte, David; Pinto, Iola; Vieira, PedroWith the ongoing growth on mobile networks utilization, new challenges come up in order to achieve a better efficient resource network management. The purpose of this paper is to present a multi-service platform based on admission curves for Third Generation (3G) and beyond mobile networks, depending on some cell characteristics, which are calculated based on real measurements. The model considers admission curves based on the Multidimensional Erlang-B model, which defines the maximum limit of resource utilization for a given Quality of Service (QoS), and will manage traffic between several services. The proposed method takes different specific constraints for each traffic environment based on network performance. To estimate the cell characteristics, for Voice and Packet Switched (PS) Release 99 (R99) services, a method is proposed, based on the Multiple Linear Regression model and dependent on Key Performance Indicators (KPI) taken from a live mobile network. For High Speed Downlink Packet Access (HSDPA) service, a different approach is set since there is a well defined time to transmit data (Transmission Time Interval (TTI)) along with other important features, like Channel Quality Indicator (CQI) and Block Error Rate (BLER), to be considered.
- An improved capacity model based on radio measurements for a 4G and beyond wireless networkPublication . Parracho, Diogo; Duarte, David; Pinto, Iola; Vieira, PedroThe mobile networks utilization is increasingly high, which implies a efficient resource network management coupled with a realistic capacity model. The aim of this paper is to present a capacity platform for Fourth Generation (4G) mobile networks, based on real measurements. The core of the proposed method is the deployment of a Multiple Linear Regression (MLR) model, based on propagation conditions, channel quality and delays for a specific cell. Information about how to locate the resource bottleneck and the related handling suggestions are provided. This approach outputs the maximum cell throughput at the busy hour, under realistic conditions. The method was developed using real data extracted from a live mobile network.
- Fault management preventive maintenance approach in mobile networks using sequential pattern miningPublication . Pereira, Márcio; Duarte, David; Vieira, PedroMobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault Management (FM) data. A comparative performance analysis between all the algorithms was carried out, having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the direction of preventive maintenance in mobile networks.
- A novel approach for user equipment indoor/outdoor classification in mobile networksPublication . Alves, Pedro; Saraiva, Thaína; Barandas, Marília; Duarte, David; Moreira, Dinis; Santos, Ricardo; Leonardo, Ricardo; Gamboa, Hugo; Vieira, PedroThe ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user’s environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user’s side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.
- Root cause analysis of low throughput situations using boosting algorithms and the TreeShap analysisPublication . Cilinio, M.; Duarte, David; Vieira, Pedro; Queluz, Maria Paula; Rodrigues, A.Detecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models are used to predict a failure/non-failure binary label. The influence of each counter on the overall model’s predictive performance is performed based on the TreeSHAP method. From the implemen tation of this technique, it is possible to identify the main causes of low throughput, based on the analysis of the most critical counters in fault detection. Furthermore, from the identification of these counters, it is possible to define a system for diagnosing the most probable throughput degradation cause. The described methodology allowed not only to identify and quantify low throughput situations in a live network due to the occurrence of misadjusted configuration parameters, radio problems and network capacity problems, but also to outline a process for solving them.