Browsing by Author "Sousa, M."
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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.
- Detecting and reducing interference in 3G and beyond wireless access networksPublication . Gomes, A.; Sousa, M.; Vieira, Pedro; Rodrigues, A.As wireless access networks evolve, addressing the growing subscribers demand, their complexity has been increasing too. To manage it, Mobile Network Operator (MNO)s rely more often on automatic methods, such as network planning, optimization and maintenance. In this regard, this study focuses on the development of a intra-Radio Access Technology (RAT) coverage optimization and interference reduction algorithm, applying Self-Organizing Networks (SON) concepts. A Genetic Algorithm (GA) was used to optimize multiple antenna tilt parameters, based on Drive Test (DT) measurements and network configuration. This study was applied to the 3rd Generation (3G) technology, nonetheless, it can be adapted to other RAT. It was tested, with both single and multiple cells optimization, resulting in a Radio Frequency (RF) condition improvement. In an urban scenario, the simultaneous optimization of several cells, resulted in a 50th percentile Received Signal Code Power (RSCP) and Energy per Chip on Spectral Noise Density (Ec/No) distributions improvement of 7 dB and 3 dB, respectively. Moreover, the joint optimization of the Electrical Downtilt (EDT) and Mechanical Downtilt (MDT) proved insignificant advantages over just the EDT optimization.
- 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.
- Self-diagnosing low coverage and high interference in 3G/4G radio access networks based on automatic RF measurement extractionPublication . Sousa, M.; Martins, A.; Vieira, PedroThis paper presents a new approach for automatic detection of low coverage and high interference scenarios (overshooting and pilot pollution) in Universal Mobile Telecommunications System (UMTS)/Long Term Evolution (LTE) networks. These algorithms, based on periodically extracted Drive Test (DT) measurements (or network trace information), identify the problematic cluster locations and compute harshness metrics, at cluster and cell level, quantifying the extent of the problem. Future work is in motion by adding self-optimization capabilities to the algorithms, which will automatically suggest physical and parameter optimization actions, based on the already developed harshness metrics. The proposed algorithms were validated for a live network urban scenario. 830 3rd Generation (3G) cells were self-diagnosed and performance metrics were computed. The most negative detected behaviors regards high interference control and not coverage verification.