User localization is one of the key service-enablers in broadband mobile communications. Moreover, from a different point of view, next steps towards automatic network optimization also depend upon the capability of the system to perform real-time user localization, in order to obtain the traffic distribution. The aim of this paper is to get deeper into the feasibility and accuracy of different localization mechanisms ranging from triangulation to database correlation. Call tracing data extracted from a real operating mobile network have been used to assess these algorithms after the execution of an extensive measurements campaign. Results show that enhanced triangulation offers the best performance even outperforming other more sophisticated mechanisms like fingerprinting, without introducing any change in the network and without requiring any special characteristic of the user equipment. Indeed, the lack of precision of channel estimates, which for the same position could differ up to 10 dB, introduces a large uncertainty that harms localization mechanisms based on database correlation. Finally, this paper identifies the areas for improvement in triangulation to reach its maximum potential, provides details for its implementation and analyzes the performance of the different proposed enhancements.
Long term evolution advanced (LTE-A) has emerged as a promising mobile broadband access technology aiming to cope with the increasing traffic demand in wireless networks. However, the enhanced spectral efficiency offered by LTE-A may become futile without a better management of scarce and overcrowded electromagnetic spectrum. In this sense, cognitive radio (CR) has been proposed as a potential solution to the problem of spectrum scarcity. Among all the mechanisms provided by CR, opportunistic spectrum access (OSA) aims at a dynamic and seamless use of certain licensed bands provided the licensee is not harmfully affected. This operation requires spectral awareness in order to avoid interferences with licensed systems. In spite of implementing some spectrum sensing mechanisms, LTE-A technology lacks other tools that are needed in order to improve the knowledge of the radio environment. This work studies the adoption of a Geo-located data base (Geo-DB) that cooperatively retrieves and maintains information regarding the location of unutilized portions of spectrum potentially available for OSA. Moreover, the potential benefit of this LTE-compliant OSA solution is evaluated using a calibrated simulation tool, by which numerical results allow us to optimally configure the system and show that the proposed opportunistic system is able to significantly improve its performance.
There are three pillars that characterize the new 5G revolution, namely, the use of heterogeneous wireless access technologies conforming an ultra-dense network, the software-driven flexibility of this network, and the simplified and user-centric operation and management of the system. This next-generation network operation and management shall be based on the usage of Big Data Analytics techniques to monitor the end-user quality of experience through direct measures of the network. This paper describes the Astellia approach towards this network revolution and presents some results on the performance of quality estimation techniques in current cellular networks. Thanks to the use of this approach, operators may fill the gap of knowledge between network key performance indicators and user experience. This way, they can operate in a proactive manner and have actual measurements of the users' experience, which leads to a fairer judgement of the users' complaints.
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