Percorrer por autor "Queluz, M. P."
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- Antenna tilt optimization using a novel QoE model based on 3G radio measurementsPublication . Pedras, V.; Sousa, M.; Vieira, Pedro; Queluz, M. P.; Rodrigues, A.This paper presents a novel Quality of Experience model (QoE), for 3G voice calls; it estimates a user perceived quality, in a Mean Opinion Score (MOS) scale, by evaluating several Radio Frequency (RF) channel metrics. Real Drive Test (DT) data with MOS measurements have been used as reference data, in order to produce a new MOS prediction model, using machine learning techniques. The new developed model enables the application of QoE as a possible network optimization criteria. Furthermore, it is showcased a generic framework to optimize antenna physical parameters. Using this framework, an algorithm was implemented which empowers the network MOS estimation by using the developed QoE model. A Root Mean Square Error (RMSE) of 9% was achieved, on the MOS prediction, using the developed model. Concerning the MOS antenna physical parameters optimization, it resulted in an average network performance gain of 5% comparatively to a interference control generic approach.
- A no-reference user centric QoE model for voice and web browsing based on 3G/4G radio measurementsPublication . Pedras, V.; Sousa, M.; Vieira, Pedro; Queluz, M. P.; Rodrigues, A.This paper presents a novel Quality of Experience (QoE) prediction model, for voice and web browsing based on real Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) data. It estimates the user perceived quality, in a Mean Opinion Score (MOS) scale, by evaluating several Radio Frequency (RF) channel measurements and Quality of Service (QoS) metrics. Real Drive Test (DT) data and MOS measurements were used as reference data, in order to produce a new QoE prediction model, using machine learning techniques. The Support Vector Regression (SVR) algorithm was used to map the QoS metrics into MOS. The new developed model enables the application of QoE as a more realistic network optimization criteria. The QoE model for voice calls presented a Root Mean Square Error (RMSE) of 11% and a correlation of 62%, when comparing the predicted MOS to the one that was measured. The web browsing model showed an higher correlation (of 92%) and a lower RMSE (of 10%).
- A no-reference video streaming QoE estimator based on physical layer 4G radio measurementsPublication . Moura, D.; Sousa, M.; Vieira, Pedro; Rodrigues, A.; Queluz, M. P.With the increase in consumption of multimedia content through mobile devices (e.g., smartphones), it is crucial to find new ways of optimizing current and future wireless networks and to continuously give users a better Quality of Experience (QoE) when accessing that content. To achieve this goal, it is necessary to provide Mobile Network Operator (MNO) with real time QoE monitoring for multimedia services (e.g., video streaming, web browsing), enabling a fast network optimization and an effective resource management. This paper proposes a new QoE prediction model for video streaming services over 4G networks, using layer 1 (i.e., Physical Layer) key performance indicators (KPIs). The model estimates the service Mean Opinion Score (MOS) based on a Machine Learning (ML) algorithm, and using real MNO drive test (DT) data, where both application layer and layer 1 metrics are available. From the several considered ML algorithms, the Gradient Tree Boosting (GTB) showed the best performance, achieving a Pearson correlation of 78.9%, a Spearman correlation of 66.8% and a Mean Squared Error (MSE) of 0.114, on a test set with 901 examples. Finally, the proposed model was tested with new DT data together with the network’s configuration. With the use case results, QoE predictions were analyzed according to the context in which the session was established, the radio transmission environment and radio channel quality indicators.
- A novel localization C# framework for RAN optimization using extreme programming methodologiesPublication . Borralho, R.; Ferreira, S.; Vieira, Pedro; Queluz, M. P.; Rodrigues, A.With the current traffic increase in cellular networks, it becomes demanding and complex for operators to manage and optimize its networks in order to provide enhanced Quality of Service (QoS) to users. One of the network optimization option is to use drive-tests data. Unfortunately, drive-tests are cost demanding, and are limited to the outdoor environments where radio measurements have been performed. A more cost effective solution is proposed in this paper as an alternative to drive-tests. A framework that gathers and locates exchanged signalling (called traces) between the real network users and network elements is proposed, enabling indoor/outdoor network optimization. To achieve that, a C# application was developed, using Extreme Programming methodologies, which were applied to produce an high quality and robust application. After event localization, a comparison was performed with drive tests data, presenting a 0.3 dB and 2.4 dB average and median error, respectively.