Automatic backhaul planning for 5G Open RAN Networks based on MNO Data

The Quality of Service (QoS) requirements for the 5th Generation (5G) services are ambitious and broad, particularly for the latency targets. To cover those, a flexible and cost-efficient Radio Access Network (RAN) is essential as proposed by the Open-RAN (O-RAN) concept. In addition, the deployment of O-RAN 5G networks can be expedited by considering network access, aggregation, and core locations of legacy technologies, where physical requisites as power supply, fiber optic links, and others are already met. With this in mind, this paper extends previous simulation work that proposed a radio network planning algorithm for 5G Millimeter Wave (mmWave) small cells to O-RAN-based networks. The backhaul planning algorithm considers both the 5G/O-RAN QoS constraints, a real 4th Generation (4G) network topology, and the respective Key Performance Indicators (KPIs) from a Mobile Network Operator (MNO) as the foundation to plan an O-RAN compliant backhaul network. Our findings identified that the latency of current networks is greatly determined by the network load. In the utmost case, comparing the network baseline and busy hour KPIs, the baseline planned O-RAN network requires 7%of the equivalent busy hour network nodes. This approach has the potential to help MNOs to outline an enlightened strategy, minimizing Capital Expenditure (CAPEX) and augmenting QoS towards upgrading legacy networks to O-RAN 5G networks.info:eu-repo/semantics/publishedVersio

Desenvolvimento de modelos de capacidade para redes móveis 3G e 4G usando dados de desempenho reais

Dissertação para obtenção do grau de mestre em Engenharia Eletrónica e Telecomunicações na área de especialização em TelecomunicaçõesA utilização de redes móveis é cada vez mais intensa e com exigências maiores, por parte dos utilizadores, o que exige uma gestão de recursos da rede mais eficiente, juntamente com modelos de capacidade realistas. O aumento da população nas grandes cidades força os operadores a aumentar a densidade das células para conseguir servir os utilizadores com bons níveis de Qualidade de Serviço (Quality of Service (QoS)), o que implica um maior investimento, por parte dos operadores, nos sistemas de telecomunicações. Assim, considera-se pertinente o desenvolvimento de estudos que viabilizem a obtenção de planos estratégicos sobre otimização dos sistemas ou, pelo menos, um conhecimento mais pormenorizado dos mesmos, ao nível da capacidade. Tais estudos suportam a tomada de decisão no que respeita à configuração das redes, compatível com a concretização de objetivos de poupança energética e de possíveis limites de capacidade, de forma a permitir, aos operadores, investimentos mais assertivos e de menor risco. O trabalho desenvolvido nesta dissertação visa propor modelos de capacidade para estações base Terceira Geração (3G) e 4G, numa tentativa de modelo unificado multitecnologia. A investigação surgiu no âmbito de uma rede real, permitindo verificar o efeito da variação do tráfego, voz e dados, na potência transmitida de uma estação base, bem como na utilização dos recursos rádio, sendo estes os pontos de partida para os modelos. O objetivo, para o modelo de capacidade 3G, é apresentar uma plataforma multi-serviços baseada em curvas de admissão, dependendo de algumas características das células, que são calculadas com base em medidas reais. O modelo considera as curvas de admissão baseadas no modelo Multidimensional Erlang-B, que define o limite máximo de utilização de recursos para um determinado QoS, permitindo gerir o tráfego entre vários serviços. O método proposto assume diferentes restrições específicas para cada ambiente de tráfego, tendo por base o desempenho da rede. Para estimar as características da célula, para os serviços de voz e Packet Switched (PS) Release 99 (R99), é proposto um método, baseado no modelo de Regressão Linear Múltipla (RLM) que depende de Key Performance Indicators (KPI)s coletados a partir de uma rede móvel real. Para o serviço High Speed Downlink Packet Access (HSDPA), é definida uma abordagem diferente, pois há um tempo bem definido para transmitir dados (Transmission Time Interval (TTI)) juntamente com outros parâmetros importantes, como o Channel Quality Indicator (CQI) e a Block Error Rate (BLER), que devem ser considerados. Relativamente ao modelo de capacidade 4G, o objetivo é apresentar uma plataforma de capacidade baseada, igualmente, em medições reais. A essência do método proposto é a implementação de um modelo de RLM, baseado em condições de propagação, qualidade de canal e atrasos para uma célula específica. São fornecidas informações sobre as possíveis limitações de recursos e algumas sugestões de melhorias do sistema, de forma a eliminar essas limitações. Esta abordagem gera o débito máximo da célula, em busy hour, tendo em conta condições realistas. De modo a validar os resultados dos modelos RLM, foram utilizadas métricas como a correlação de Pearson, Mean Absolute Percentage Error (MAPE) e Root Mean Square Error (RMSE). Para o modelo de capacidade 3G, para duas células, obtiveram-se valores do R2 ajustado superiores a 84,52%, de MAPE inferiores a 0,91% e do coeficiente de correlação superiores a 81,43%. Também se conseguiu uma redução da potencia transmitida da célula, superior a 20% face à potencia máxima da célula. No modelo de capacidade 4G foi possível detetar problemas de capacidade em nove células, numa análise de 89 no total. Nos modelos RLM aplicados obtiveram-se valores do R2 ajustado superiores a 92,8%, de MAPE inferiores a 10,25% e do coeficiente de correlação de 96,36%.The mobile networks utilization is increasingly high and with greater demands, by users, which implies a better efficient resource network management coupled with a realistic capacity model. Increased population in large urban cities forces operators to increase cell density to serve users with good QoS levels, which implies a greater investment, by the operators, in telecommunication systems. Therefore, it is considered relevant to develop studies that make it possible to obtain strategic plans on system optimization or, at least, a more detailed knowledge of it, at a capacity level. These studies support decision making with regard to the networks configuration compatible with the energy saving achievement targets and possible capacity limits, in order to allow operators to make more assertive and less risky investments. In this dissertation, the developed work aims to propose capacity models for 3G and 4G base stations, in a multi-technology unified model attempt. The research was carried out within a real network, allowing to verify the effect of the voice and data traffic variation on the base station transmitted power, as well as the use of radio resources, which are the models starting points. For 3G capacity model, the objective is to present a multi-service platform based on admission curves, depending on some cell characteristics, which are calculated based on real measurements. The model considers admission curves based on the Multidimensional Erlang-B model, which defines the maximum limit of resource utilization for a given QoS, and will manage traffic between several services. The proposed method takes different specific constraints for each traffic environment based on network performance. To estimate the cell characteristics, for Voice and Packet Switched (PS) Release 99 (R99) services, a method is proposed, based on the Multiple Linear Regression model and dependent on Key Performance Indicators (KPI) taken from a live mobile network. For High Speed Downlink Packet Access (HSDPA) service, a different approach is set since there is a well defined time to transmit data (TTI) along with other important features, like CQI and BLER, to be considered. Regarding the 4G capacity model, the objective is to present a capacity platform based also on real measurements. The core of the proposed method is the deployment of a Multiple Linear Regression (MLR) model, based on propagation conditions, channel quality and delays for a specific cell. Information about how to locate the resource bottleneck and the related handling suggestions are provided. This approach outputs the maximum cell throughput at the busy hour under realistic conditions. The method was developed using real data extracted from a live mobile network. In order to proceed with validation, metrics such as the Pearson correlation, MAPE and RMSE were used. For the 3G capacity model, for two cells, values of adjusted R2 higher than 84,52%, MAPE lower than 0,91% and correlation coefficient higher than 81,43% were obtained. A reduction in cell transmitted power was also achieved, over 20% against the cell maximum power. In the 4G capacity model it was possible to detect capacity problems in nine cells, in 89 total analysed cells. In the applied MLR models, values of adjusted R2 higher than 92,8%, MAPE lower than 10,25% and correlation coefficient higher than 96,36% were obtained.N/

An enhanced capacity model based on network measurements for a multi-service 3G system

With the ongoing growth on mobile networks utilization, new challenges come up in order to achieve a better efficient resource network management. The purpose of this paper is to present a multi-service platform based on admission curves for Third Generation (3G) and beyond mobile networks, depending on some cell characteristics, which are calculated based on real measurements. The model considers admission curves based on the Multidimensional Erlang-B model, which defines the maximum limit of resource utilization for a given Quality of Service (QoS), and will manage traffic between several services. The proposed method takes different specific constraints for each traffic environment based on network performance. To estimate the cell characteristics, for Voice and Packet Switched (PS) Release 99 (R99) services, a method is proposed, based on the Multiple Linear Regression model and dependent on Key Performance Indicators (KPI) taken from a live mobile network. For High Speed Downlink Packet Access (HSDPA) service, a different approach is set since there is a well defined time to transmit data (Transmission Time Interval (TTI)) along with other important features, like Channel Quality Indicator (CQI) and Block Error Rate (BLER), to be considered.info:eu-repo/semantics/publishedVersio

An improved capacity model based on radio measurements for a 4G and beyond wireless network

The mobile networks utilization is increasingly high, which implies a efficient resource network management coupled with a realistic capacity model. The aim of this paper is to present a capacity platform for Fourth Generation (4G) mobile networks, based on real measurements. The core of the proposed method is the deployment of a Multiple Linear Regression (MLR) model, based on propagation conditions, channel quality and delays for a specific cell. Information about how to locate the resource bottleneck and the related handling suggestions are provided. This approach outputs the maximum cell throughput at the busy hour, under realistic conditions. The method was developed using real data extracted from a live mobile network.info:eu-repo/semantics/publishedVersio

BIM and BEM Interoperability–Evaluation of a Case Study in Modular Wooden Housing

Building information modelling (BIM) is the first step towards implementing Building 4.0, where virtual reality and digital twins are key elements. The use of unmanned aircraft systems (UAS/drones) to capture data from buildings is nowadays a very popular method, so a methodology was developed to digitally integrate the photogrammetric surveys of a building into BIM, exclusively with the use of drones. Currently, buildings are responsible for 40% of energy consumption in Europe; therefore, the interconnection between BIM and building energy modelling (BEM) is essential to digitalize the construction sector, increasing competitiveness through cost reduction. In this context, the BlueWoodenHouse Project aims, among other activities, to characterize the solutions/systems of building materials and monitor the temperature, relative humidity and CO2, as well as energy consumption, of a single-family modular wooden house located in the north of Portugal, with 190 m2 and three users. Thus, the experimental monitoring results, of this case study, were used to validate the numerical model developed in the DesignBuilder simulator, which includes the building envelope’s 3D geometrical data obtained by one of those aircraft, in order to demonstrate the usefulness of drones for the optimization of solutions, from the energy point of view