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Browsing by Issue Date, starting with "2018-02-27"

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  • A novel localization C# framework for RAN optimization using extreme programming methodologies
    Publication . 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.
    2018-02-27Conference object Restricted access Show more
  • Antenna tilt optimization using a novel QoE model based on 3G radio measurements
    Publication . 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.
    2018-02-27Conference object Restricted access Show more