Browsing by Author "Rodrigues, António"
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- An efficient approach for capacity savings using load balancing in dual layer 3G wireless networksPublication . Pedro, Tiago; Martins, André; Rodrigues, António; Vieira, PedroIn order to survive in a highly competitive market, mobile network operators have to be as efficient as possible in managing their resources. This is particularly relevant in what concerns the capacity available at their sites. This work aims to give the operators a method to improve longevity of their sites. This was achieved using a Load Balancing algorithm, which takes into consideration the Channel Element usage of sites and sets an Received Signal Code Power threshold value for each one. Its evaluation is done by using a Traffic Forecast algorithm, based on a fitting method, in order to obtain an estimate of when the sites' capacity limit is reached, before and after applying Load Balancing. The used input data consisted of real traffic statistics, including geo-located indicators. During the course of this work it was possible to develop a semi-automatic method for network optimization using geo-located data, thus making a contribute to the development of national research on Self-Organizing Networks. This project was developed in collaboration with a Portuguese telecommunications consulting company, Celfinet, which provided valuable supervision and guidance. Using the suggested method it is predicted that, after a year of implementation, it is possible to achieve savings of about 70% in capacity expansions in the network.
- An ubiquitous 2.6 GHz radio propagation model for wireless networks using self-supervised learning from satellite imagesPublication . Sousa, Marco; Vieira, Pedro; Queluz, Maria Paula; Rodrigues, AntónioThe performance of any Mobile Wireless Network (MWN) is dependent on the appropriate level of radio coverage, with Path Loss (PL) models being a valuable resource for its evaluation. Recently, advancements in Machine Learning (ML) and Deep Neural Networks (DNNs) have been applied to radio propagation to produce new data-driven PL models. Notoriously, these advancements have also allowed the inclusion of non-classical inputs, such as satellite images. However, data-driven PL models are often developed under the assumption that training and test data distributions are similar, which is a weak assumption in real-world scenarios. Thus, generalization (i.e., the model’s ability to perform on different data distributions) is a crucial aspect of data-driven PL models in the context of Mobile Network Operators (MNOs). This paper proposes a new data-driven PL model, the Ubiquitous Satellite Aided Radio Propagation (USARP) model, developed to enhance the geographical generalization capabilities of empirical PL models, by using satellite images. The USARP model considers self-supervised learning to extract general data representations of the radio environment from satellite images, improving the PL prediction Root Mean Square Error (RMSE) of the 3rd Generation Partnership Project (3GPP) PL model in the order of 9 dB, and for a data distribution distinct from the training data. Moreover, it was demonstrated the potential of the USARP model in terms of geographical and radio environment generalization. Although the generalization capabilities of ML regression algorithms are limited, the chosen USARP architecture and the use of regularization techniques had a positive impact on its geographical generalization performance.
- Analysis and optimization of 5G coverage predictions using a beamforming antenna model and real drive test measurementsPublication . Sousa, Marco; Alves, André; Vieira, Pedro; Queluz, Maria Paula; Rodrigues, AntónioThe ability to estimate radio coverage accurately is fundamental for planning and optimizing any wireless network, notably when a new generation, as the 5(th) Generation (5G), is in an early deployment phase. The knowledge acquired from radio planning of previous generations must be revisited, particularly the used path loss and antennas models, as the 5G propagation is intrinsically distinct. This paper analyses a new beamforming antenna model and distinct path loss models - 3(rd) Generation Partnership Project (3GPP) and Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications (mmMAGIC) - applying them to evaluate 5G coverage in 3-Dimensional (3D) synthetic and real scenarios, for outdoor and indoor environments. Further, real 5G Drive Tests (DTs) were used to evaluate the 3GPP path loss model accuracy in Urban Macro (UMa) scenarios. For the new antenna model, it is shown that the use of beamforming with multiple vertical beams is advantageous when the Base Station (BS) is placed below the surrounding buildings; in regular UMa surroundings, one vertical beam provides adequate indoor coverage and a maximized outdoor coverage after antenna tilt optimization. The 3GPP path loss model exhibited a Mean Absolute Error (MAE) of 21.05 dB for Line-of-Sight (LoS) and 14.48 dB for Non-Line-of-Sight (NLoS), compared with real measurements. After calibration, the MAE for LoS and NLoS decreased to 5.45 dB and 7.51 dB, respectively. Moreover, the non-calibrated 3GPP path loss model led to overestimations of the 5G coverage and user throughput up to 25% and 163%, respectively, when compared to the calibrated model predictions. The use of Machine Learning (ML) algorithms resulted in path loss MAEs within the range of 4.58 dB to 5.38 dB, for LoS, and within the range of 3.70 dB to 5.96 dB, for NLoS, with the Random Forest (RF) algorithm attaining the lowest error.
- Automatic tunning of Okumura-Hata model on railway communicationsPublication . Beire, Ana Rita; Cota, Nuno; Pinheiro Pita, Helder Jorge; Rodrigues, AntónioThis paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.
- Capacity enhancement using MIMO antenna arrays in realistic macro-cellular urban environmentPublication . Vieira, Pedro; Queluz, Paula; Rodrigues, AntónioIn MIMO systems the antenna array configuration in the BS and MS has a large influence on the available channel capacity. In this paper, we first introduce a new Frequency Selective (FS) MIMO framework for macro-cells in a realistic urban environment. The MIMO channel is built over a previously developed directional channel model, which considers the terrain and clutter information in the cluster, line-of-sight and link loss calculations. Next, MIMO configuration characteristics are investigated in order to maximize capacity, mainly the number of antennas, inter-antenna spacing and SNR impact. Channel and capacity simulation results are presented for the city of Lisbon, Portugal, using different antenna configurations. Two power allocations schemes are considered, uniform distribution and FS spatial water-filling. The results suggest optimized MIMO configurations, considering the antenna array size limitations, specially at the MS side.
- Improving a cluster based directional channel model in realistic macro-cell environmentPublication . Vieira, Pedro; Queluz, Maria Paula; Rodrigues, AntónioIn 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.
- Improving accuracy for OTD based 3G geolocation in real urban/suburban environmentsPublication . Vieira, Pedro; Silva, Nuno Oliveira e; Fernandes, Nuno; Rodrigues, António; Varela, LuísThis paper presents the recent research results about the development of a Observed Time Difference (OTD) based geolocation algorithm based on network trace data, for a real Universal Mobile Telecommunication System (UMTS) Network. The initial results have been published in [1], the current paper focus on increasing the sample convergence rate, and introducing a new filtering approach based on a moving average spatial filter, to increase accuracy. Field tests have been carried out for two radio environments (urban and suburban) in the Lisbon area, Portugal. The new enhancements produced a geopositioning success rate of 47% and 31%, and a median accuracy of 151 m and 337 m, for the urban and suburban environments, respectively. The implemented filter produced a 16% and 20% increase on accuracy, when compared with the geopositioned raw data. The obtained results are rather promising in accuracy and geolocation success rate. OTD positioning smoothed by moving average spatial filtering reveals a strong approach for positioning trace extracted events, vital for boosting Self-Organizing Networks (SON) over a 3G network.
- Lisbon mobility simulations for performance evaluation of mobile networksPublication . Vieira, Pedro; Vieira, Manuel; Queluz, Maria Paula; Rodrigues, AntónioIn 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.
- A machine learning driven methodology for alarm prediction towards self-healing in wireless networksPublication . Mata, Luís; Sousa, Marco; Vieira, Pedro; Queluz, Maria Paula; Rodrigues, AntónioAlthough Artificial Intelligence (AI) is already used by 5th Generation (5G) to support specific network functions, the increased complexity of 6th Generation (6G) will demand the adoption of extended AI capabilities to enhance network efficiency. Moreover, high network performance and availability at a sustainable cost will be crucial to emerging applications, such as autonomous vehicles and smart cities. In this context, operators are expected to implement Self-Healing Operations (SHOs) to transition from reactive handling of network faults to a preventive approach, relying on statistical learning of network data. This paper proposes a Machine Learning (ML)-driven methodology to predict network faults using generic Fault Management (FM) data, enabling the implementation of preventive actions to avoid service degradation or failure. The evaluation of this methodology using live network data revealed statistical associations among certain network faults, considering both time and root-cause factors. Therefore, FM data and two ML models, namely Logistic Regression (LR) and Light Gradient Boosting Model (LGBM), were used to predict network faults, achieving a 93% success rate within a 60-minute anticipation period.
- Neighbour list optimization for real LTE radio networkPublication . Duarte, David Eduardo Correia; Vieira, Pedro; Rodrigues, António; Martins, A.; Oliveira, N.; Varela LuísWith the increasing complexity of current 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 Neighbour Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). An algorithm composed by three decisions were were developed: distance-based, Radio Frequency (RF) measurement-based and Handover (HO) stats-based. The distance-based decision, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB and neighbour relation addition/removal based on user defined constraints. The algorithms were developed and implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. © 2014 IEEE.