Improving a cluster based directional channel model in realistic macro-cell environment

In this paper a realistic directional channel model that is an extension of the COST 273 channel model is presented. The model uses a cluster of scatterers and visibility region generation based strategy with increased realism, due to the introduction of terrain and clutter information. New approaches for path-loss prediction and line of sight modeling are considered, affecting the cluster path gain model implementation. The new model was implemented using terrain, clutter, street and user mobility information for the city of Lisbon, Portugal. Some of the model's outputs are presented, mainly path loss and small/large-scale fading statistics

Lisbon mobility simulations for performance evaluation of mobile networks

In this paper a novel realistic vehicular mobility model is introduced. It captures the moving-in-groups, conscious travelling, and introduces the concept of smart travelling while following drivers’ social behavior extracted from inquiries and experimental traffic measurements. Under the model, a routing algorithm is considered. The routing algorithm minimizes the distance to a target on a step by step form, in every street crossing. This is done under a hierarchic street level structure that optimizes travel speed and quality. The mobility model was simulated for Lisbon case study and directional statistical results were compared with experimental measurements from Lisbon Municipality control center. The output shows a good correlation between simulated and experimental values.info:eu-repo/semantics/publishedVersio

Root cause analysis of low throughput situations using boosting algorithms and the TreeShap analysis

Detecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models are used to predict a failure/non-failure binary label. The influence of each counter on the overall model’s predictive performance is performed based on the TreeSHAP method. From the implemen tation of this technique, it is possible to identify the main causes of low throughput, based on the analysis of the most critical counters in fault detection. Furthermore, from the identification of these counters, it is possible to define a system for diagnosing the most probable throughput degradation cause. The described methodology allowed not only to identify and quantify low throughput situations in a live network due to the occurrence of misadjusted configuration parameters, radio problems and network capacity problems, but also to outline a process for solving them.info:eu-repo/semantics/publishedVersio

Evaluating 5G coverage in 3D scenarios under configurable antenna beam patterns

Active Antenna Systems (AASs) play a key role in the performance of 5 th Generation (5G) networks as they enable the use of Massive Multiple-Input Multiple-Output (mMIMO) and directional beamforming. Besides, AASs can be configured with distinct broadcast beams configurations. In this work, the coverage provided by the broadcast beam configurations of a real AAS is evaluated. A 3-Dimensional (3D) configurable synthetic scenario was proposed to evaluate the resulting 5G coverage from all the possible antenna beam configurations. This analysis revealed that beam configurations with several horizontal beams and one vertical are recommended for urban macro deployments. Moreover, it was demonstrated that the percentage of covered area in a real scenario is approximated by an equivalent synthetic scenario with a Pearson correlation of 0.98. The synthetic scenario has the advantage of not requiring 3D building databases. Finally, an interference analysis in multi-site real scenarios was conducted, where it was verified that some antenna configurations introduce excessive interference for the level of coverage provided.info:eu-repo/semantics/publishedVersio

Developing a new simulation and visualization platform for researching aspects of mobile network performance

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.info:eu-repo/semantics/publishedVersio

Towards the use of Unsupervised Causal Learning in Wireless Networks Operation

The current paradigm in Mobile Wireless Networks (MWNs) operation is being defied by the increasing importance of Machine Learning (ML) and Artificial Intelligence (AI). Nevertheless, another paradigm shift is rising with recent developments in causal inference and causal discovery, which, although having the potential to be applied to MWNs, have been relatively unexplored. This paper aims to develop a data-driven methodology using unsupervised ML and Conditional Independence Tests (CITs), typically used in causal discovery tasks, to identify distinct network performance patterns and pinpoint causal factors to explain them. The proposed methodology was first evaluated with crowdsourcing data from User Equipments (UEs). Afterwards, a dataset from a Long-Term Evolution (LTE) network, composed of a set of arbitrary performance indicators and configuration parameters, was considered. The crowdsourcing dataset, containing multiple network speed tests, revealed that the measured uplink throughput contributed the most to the observed performance patterns due to the used Radio Access Technologies (RATs). Furthermore, the LTE dataset revealed a causal relationship between the number of reserved signalling resources in the Physical Uplink Control Channel (PUCCH) and the UE uplink throughput. Notwithstanding, the key contribution of this paper is the consideration of causal-based concepts and methods for network operations enhancement

NeRF-QA: Neural Radiance Fields Quality Assessment Database

This short paper proposes a new database - NeRF-QA - containing 48 videos synthesized with seven NeRF based methods, along with their perceived quality scores, resulting from subjective assessment tests; for the videos selection, both real and synthetic, 360 degrees scenes were considered. This database will allow to evaluate the suitability, to NeRF based synthesized views, of existing objective quality metrics and also the development of new quality metrics, specific for this case