Browsing by Author "Duarte, D."
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- An enhanced proposal in neighbor list planning for LTE SON radio access networksPublication . Duarte, D.; Martins, A.; Vieira, Pedro; Rodrigues, A.; Silva, N.Nowadays, a coexistence of 2nd Generation (2G), 3rd Generation (3G) and 4th Generation (4G) networks is being witnessed. Due to this situation, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper presents the latest work around an algorithm that optimizes the Neighbor Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). The algorithm is based on intra-site distance, its antenna orientation (azimuth/elevation), radiation pattern and overlap areas between cells. The research initial steps were already published (Duarte, D.; Vieira, P.; Rodrigues, A.; Martins, A.; Oliveira, N.; Varela, L., “Neighbour List Optimization for Real LTE Radio Networks,” Wireless and Mobile, 2014 IEEE Asia Pacific Conference on, pp.183,187, 28-30 Aug. 2014).
- Developing a new simulation and visualization platform for researching aspects of mobile network performancePublication . Amaro, C.; Saraiva, T.; Duarte, D.; Vieira, Pedro; Queluz, Maria Paula; Rodrigues, A.Nowadays, mobile networks represent one of the most innovative and challenging technological and research-oriented fields of work. The growth on user subscriptions and the advances introduced by Artificial Intelligence (AI) and Internet of Things (IoT), greatly enhanced the complexity and potential of communication networks. The increase on variety of devices and exchanged mobile data traffic resulted in demanding requirements for the network providers. As networks tend to scale and data to increase, some problems start to arise. Traffic congestion, packet loss and high latency being some examples. Therefore, it is important to introduce powerful tools and methods to tackle these challenges. On this perspective, several studies have highlighted AI systems, mainly Machine Learning (ML) algorithms, as the most promising methods, in the context of wireless networks, by improving the overall performance and efficiency. This work proposes to integrate several network optimization algorithms, already developed, in a common and unified visualization platform. These algorithms were developed in C# and Python and some of them use supervised and unsupervised ML techniques. The proposed solution includes multi-threading processes to deal with concurrent simulations, a proxy to communicate between platforms and a dynamic visual interface.
- A hybrid neighbor optimization algorithm for SON based on network topology, Handover counters and RF measurementsPublication . Duarte, D.; Martins, A.; Vieira, Pedro; Rodrigues, A.With the increasing complexity of current wireless networks, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper aims to optimize the Neighbor Cell List (NCL) for radio network cells. An algorithm composed by three decision criteria was developed: geographic localization and orientation, according network topology, Radio Frequency (RF) measurements collected by drive-tests or traces and Performance Management (PM) counters from Handover (HO) statistics. The first decision, proposes a new NCL taking into account the Base Station (BS) location and interference tiers, based on the quadrant method. The last two decision criteria consider signal strength and interference level measurements and HO statistics in a time period, respectively. They also define a priority to each cell and added, kept or removed neighbor relation, based on user defined constraints. The algorithms were developed and implemented over new radio network optimization professional tool. Several case studies were produced using real data from a mobile operator.