Até 2020 espera-se que as redes celulares aumentam em dez vezes a área de cobertura, suporte cem vezes mais equipamentos de usuários e eleve a capacidade da taxa de dados em mil vezes, comparada as redes celulares atuais. A densa implantação de pequenas células é considerada uma solução promissora para alcançar essas melhorias, uma vez que aproximar as antenas dos usuários proporciona maiores taxas de dados, devido à qualidade do sinal em curtas distâncias. No entanto, operar um grande número de antenas pode aumentar significativamente o consumo de energia da infraestrutura de rede. Além disso, a grande inserção de novos rádios pode ocasionar maior interferência espectral entre as células. Nesse cenário, a gestão dos recursos de rádio é essencial devido ao impacto na qualidade do serviço prestado aos usuários. Por exemplo, baixas potências de transmissão podem deixar usuários sem conexão, enquanto altas potências elevam a possibilidade de ocorrência de interferência. Além disso, a reutilização não planejada dos recursos de rádio causa a ocorrência de interferência, resultando em baixa capacidade de transmissão, enquanto a subutilização de recursos limita a capacidade total de transmissão de dados. Uma solução para controlar a potência de transmissão, atribuir os recursos de rádio e garantir o serviço aos usuários é essencial. Nesta dissertação, é proposto um algoritmo adaptativo de Monte Carlo para realizar alocação global de recursos de forma eficiente em termos de energia, para arquiteturas Heterogeneous Cloud Radio Access Network (H-CRAN), projetadas como futuras redes de quinta geração (5G). Uma solução eficiente para a alocação de recursos em cenários de alta e baixa densidade é proposta. Nossas contribuições são triplas: (i) proposta de uma abordagem global para o problema de atribuição de recursos de rádio na arquitetura HCRAN, cujo caráter estocástico garante uma amostragem geral de espaço de solução; (ii) uma comparação crítica entre nossa solução global e um modelo local; (iii) a demonstração de que, para cenários de alta densidade, a Eficiência Energética não é uma medida adequada para alocação eficiente, considerando a capacidade de transmissão, justiça e total de usuários atendidos. Além disso, a proposta é comparada em relação a três algoritmos de alocação de recursos de última geração para redes 5G. / Up until 2020 it is expected that cellular networks must raise the coverage area in 10-fold, support a 100-fold more user equipments, and increase the data rate capacity by a 1000-fold in comparison with current cellular networks. The dense deployment of small cells is considered a promising solution to reach such aggressive improvements, once it moves the antennas closer to the users, achieving higher data rates due to the signal quality at short distances. However, operating a massive number of antennas can significantly increase the energy consumption of the network infrastructure. Furthermore, the large insertion of new radios brings greater spectral interference between the cells. In this scenery, the optimal management of radio resources turn an exaction due to the impact on the quality of service provided to the users. For example, low transmission powers can leave users without connection, while high transmission powers can contribute to inter radios interference. Furthermore, the interference can be raised on the unplanned reuse of the radio resources, resulting in low data transmission per radio resource, as the under-reuse of radio resources limits the overall data transmission capacity. A solution to control the transmission power, assign the spectral radio resources, and ensure the service to the users is essential. In this thesis, we propose an Adaptive Monte Carlo algorithm to perform global energy efficient resource allocation for Heterogeneous Cloud Radio Access Network (HCRAN) architectures, which are forecast as future fifth-generation (5G) networks. We argue that our global proposal offers an efficient solution to the resource allocation for both high and low density scenarios. Our contributions are threefold: (i) the proposal of a global approach to the radio resource assignment problem in H-CRAN architecture, whose stochastic character ensures an overall solution space sampling; (ii) a critical comparison between our global solution and a local model; (iii) the demonstration that, for high density scenarios, Energy Efficiency is not a well suited metric for efficient allocation, considering data rate capacity, fairness, and served users. Moreover, we compare our proposal against three state-of-the-art resource allocation algorithms for 5G networks.

<p>After more than two decades’ development, modern mobile cellular networks now have almost approached to the commercial level of fourth generation communication networks. For each of the mobile solutions, there are special attributes but also similarities compared to the other competitive solutions. We can also find relationships between the old generation solutions and the inheritors or innovations.</p><p>During the communicating session using any kind of existing mobile handset, the handover procedure is a very important one that may effect connection quality and also the phone call continuity. Nowadays, the mobile cellular networks have a trend to interact with LAN networks. They will co-exist and work together to support higher data rate over a wider coverage. Seamless handover proposals like Unlicensed Mobile Access (UMA) can support the heterogeneous handover between Global System for Mobile Communications (GSM) and Wireless (Wi-Fi) Network. Several Media Independent Handover (MIH) proposals can handle the vertical handover in the hybrid mobile data network environment such like between wireless local area network (WLAN) and Universal Mobile Telecommunications System (UMTS) by different solutions.</p>

Large-scale blockages like buildings affect the performance of urban cellular networks, especially in the millimeter-wave frequency band. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages, and quantifies their effects on the performance of cellular networks. Specifically, random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane, which is called a Boolean scheme. The distribution of the number of blockages in a link is proven to be Poisson with parameter dependent on the length of the link, which leads to the distribution of penetration losses of a single link. A path loss model that incorporates the blockage effects is proposed, which matches experimental trends observed in prior work. The blockage model is applied to analyze blockage effects on cellular networks assuming blockages are impenetrable, in terms of connectivity, coverage probability, and average rate. Analytic results show while buildings may block the desired signal, they may still have a positive impact on network performance since they also block more interference. / text

After more than two decades’ development, modern mobile cellular networks now have almost approached to the commercial level of fourth generation communication networks. For each of the mobile solutions, there are special attributes but also similarities compared to the other competitive solutions. We can also find relationships between the old generation solutions and the inheritors or innovations. During the communicating session using any kind of existing mobile handset, the handover procedure is a very important one that may effect connection quality and also the phone call continuity. Nowadays, the mobile cellular networks have a trend to interact with LAN networks. They will co-exist and work together to support higher data rate over a wider coverage. Seamless handover proposals like Unlicensed Mobile Access (UMA) can support the heterogeneous handover between Global System for Mobile Communications (GSM) and Wireless (Wi-Fi) Network. Several Media Independent Handover (MIH) proposals can handle the vertical handover in the hybrid mobile data network environment such like between wireless local area network (WLAN) and Universal Mobile Telecommunications System (UMTS) by different solutions.

Até 2020 espera-se que as redes celulares aumentam em dez vezes a área de cobertura, suporte cem vezes mais equipamentos de usuários e eleve a capacidade da taxa de dados em mil vezes, comparada as redes celulares atuais. A densa implantação de pequenas células é considerada uma solução promissora para alcançar essas melhorias, uma vez que aproximar as antenas dos usuários proporciona maiores taxas de dados, devido à qualidade do sinal em curtas distâncias. No entanto, operar um grande número de antenas pode aumentar significativamente o consumo de energia da infraestrutura de rede. Além disso, a grande inserção de novos rádios pode ocasionar maior interferência espectral entre as células. Nesse cenário, a gestão dos recursos de rádio é essencial devido ao impacto na qualidade do serviço prestado aos usuários. Por exemplo, baixas potências de transmissão podem deixar usuários sem conexão, enquanto altas potências elevam a possibilidade de ocorrência de interferência. Além disso, a reutilização não planejada dos recursos de rádio causa a ocorrência de interferência, resultando em baixa capacidade de transmissão, enquanto a subutilização de recursos limita a capacidade total de transmissão de dados. Uma solução para controlar a potência de transmissão, atribuir os recursos de rádio e garantir o serviço aos usuários é essencial. Nesta dissertação, é proposto um algoritmo adaptativo de Monte Carlo para realizar alocação global de recursos de forma eficiente em termos de energia, para arquiteturas Heterogeneous Cloud Radio Access Network (H-CRAN), projetadas como futuras redes de quinta geração (5G). Uma solução eficiente para a alocação de recursos em cenários de alta e baixa densidade é proposta. Nossas contribuições são triplas: (i) proposta de uma abordagem global para o problema de atribuição de recursos de rádio na arquitetura HCRAN, cujo caráter estocástico garante uma amostragem geral de espaço de solução; (ii) uma comparação crítica entre nossa solução global e um modelo local; (iii) a demonstração de que, para cenários de alta densidade, a Eficiência Energética não é uma medida adequada para alocação eficiente, considerando a capacidade de transmissão, justiça e total de usuários atendidos. Além disso, a proposta é comparada em relação a três algoritmos de alocação de recursos de última geração para redes 5G. / Up until 2020 it is expected that cellular networks must raise the coverage area in 10-fold, support a 100-fold more user equipments, and increase the data rate capacity by a 1000-fold in comparison with current cellular networks. The dense deployment of small cells is considered a promising solution to reach such aggressive improvements, once it moves the antennas closer to the users, achieving higher data rates due to the signal quality at short distances. However, operating a massive number of antennas can significantly increase the energy consumption of the network infrastructure. Furthermore, the large insertion of new radios brings greater spectral interference between the cells. In this scenery, the optimal management of radio resources turn an exaction due to the impact on the quality of service provided to the users. For example, low transmission powers can leave users without connection, while high transmission powers can contribute to inter radios interference. Furthermore, the interference can be raised on the unplanned reuse of the radio resources, resulting in low data transmission per radio resource, as the under-reuse of radio resources limits the overall data transmission capacity. A solution to control the transmission power, assign the spectral radio resources, and ensure the service to the users is essential. In this thesis, we propose an Adaptive Monte Carlo algorithm to perform global energy efficient resource allocation for Heterogeneous Cloud Radio Access Network (HCRAN) architectures, which are forecast as future fifth-generation (5G) networks. We argue that our global proposal offers an efficient solution to the resource allocation for both high and low density scenarios. Our contributions are threefold: (i) the proposal of a global approach to the radio resource assignment problem in H-CRAN architecture, whose stochastic character ensures an overall solution space sampling; (ii) a critical comparison between our global solution and a local model; (iii) the demonstration that, for high density scenarios, Energy Efficiency is not a well suited metric for efficient allocation, considering data rate capacity, fairness, and served users. Moreover, we compare our proposal against three state-of-the-art resource allocation algorithms for 5G networks.

Até 2020 espera-se que as redes celulares aumentam em dez vezes a área de cobertura, suporte cem vezes mais equipamentos de usuários e eleve a capacidade da taxa de dados em mil vezes, comparada as redes celulares atuais. A densa implantação de pequenas células é considerada uma solução promissora para alcançar essas melhorias, uma vez que aproximar as antenas dos usuários proporciona maiores taxas de dados, devido à qualidade do sinal em curtas distâncias. No entanto, operar um grande número de antenas pode aumentar significativamente o consumo de energia da infraestrutura de rede. Além disso, a grande inserção de novos rádios pode ocasionar maior interferência espectral entre as células. Nesse cenário, a gestão dos recursos de rádio é essencial devido ao impacto na qualidade do serviço prestado aos usuários. Por exemplo, baixas potências de transmissão podem deixar usuários sem conexão, enquanto altas potências elevam a possibilidade de ocorrência de interferência. Além disso, a reutilização não planejada dos recursos de rádio causa a ocorrência de interferência, resultando em baixa capacidade de transmissão, enquanto a subutilização de recursos limita a capacidade total de transmissão de dados. Uma solução para controlar a potência de transmissão, atribuir os recursos de rádio e garantir o serviço aos usuários é essencial. Nesta dissertação, é proposto um algoritmo adaptativo de Monte Carlo para realizar alocação global de recursos de forma eficiente em termos de energia, para arquiteturas Heterogeneous Cloud Radio Access Network (H-CRAN), projetadas como futuras redes de quinta geração (5G). Uma solução eficiente para a alocação de recursos em cenários de alta e baixa densidade é proposta. Nossas contribuições são triplas: (i) proposta de uma abordagem global para o problema de atribuição de recursos de rádio na arquitetura HCRAN, cujo caráter estocástico garante uma amostragem geral de espaço de solução; (ii) uma comparação crítica entre nossa solução global e um modelo local; (iii) a demonstração de que, para cenários de alta densidade, a Eficiência Energética não é uma medida adequada para alocação eficiente, considerando a capacidade de transmissão, justiça e total de usuários atendidos. Além disso, a proposta é comparada em relação a três algoritmos de alocação de recursos de última geração para redes 5G. / Up until 2020 it is expected that cellular networks must raise the coverage area in 10-fold, support a 100-fold more user equipments, and increase the data rate capacity by a 1000-fold in comparison with current cellular networks. The dense deployment of small cells is considered a promising solution to reach such aggressive improvements, once it moves the antennas closer to the users, achieving higher data rates due to the signal quality at short distances. However, operating a massive number of antennas can significantly increase the energy consumption of the network infrastructure. Furthermore, the large insertion of new radios brings greater spectral interference between the cells. In this scenery, the optimal management of radio resources turn an exaction due to the impact on the quality of service provided to the users. For example, low transmission powers can leave users without connection, while high transmission powers can contribute to inter radios interference. Furthermore, the interference can be raised on the unplanned reuse of the radio resources, resulting in low data transmission per radio resource, as the under-reuse of radio resources limits the overall data transmission capacity. A solution to control the transmission power, assign the spectral radio resources, and ensure the service to the users is essential. In this thesis, we propose an Adaptive Monte Carlo algorithm to perform global energy efficient resource allocation for Heterogeneous Cloud Radio Access Network (HCRAN) architectures, which are forecast as future fifth-generation (5G) networks. We argue that our global proposal offers an efficient solution to the resource allocation for both high and low density scenarios. Our contributions are threefold: (i) the proposal of a global approach to the radio resource assignment problem in H-CRAN architecture, whose stochastic character ensures an overall solution space sampling; (ii) a critical comparison between our global solution and a local model; (iii) the demonstration that, for high density scenarios, Energy Efficiency is not a well suited metric for efficient allocation, considering data rate capacity, fairness, and served users. Moreover, we compare our proposal against three state-of-the-art resource allocation algorithms for 5G networks.

Context With the rapidly growing demand for cellular networks’ capacityand coverage, effective planning of Network Infrastructure (NI) has been amajor challenge for the telecom operators. The mobility patterns of different subscriber groups in the networks have been found to be a crucialaspect in the planning of NI. For a telecom operator, it is important to havean estimate of the efficiency (in terms of the Network Capacity - numberof subscribers that the network can handle) of the existing NI. For thispurpose, Lundberg et. al., have developed an optimization based strategycalled as Tetris Strategy (TS), based on the standard subscriber groupingapproach called MOSAIC. The objective of TS is to calculate the upperbound estimate of the efficiency of the NI. Objectives The major objective of this thesis is to compare the efficiencyvalue of the NI when the subscribers are grouped (clustered) based on theirmobility patterns (characterized by a mobile trajectory) with the efficiencyvalue obtained when the subscribers are grouped based on the standardsubscriber grouping approach - MOSAIC. Methods Literature Review (LR) has been conducted to identify the stateof the art similarity/distance measures and algorithms to cluster trajectory data. Among the identified ones, for conducting experiments, LongestCommon Subsequences has been chosen as a similarity/distance measure,and Spectral and Agglomerative clustering algorithms have been chosen.All the experiments have been conducted on the subscriber trajectory dataprovided by the telecom operator, Telenor. The clusters obtained from theexperiments have been plugged into TS, to calculate the upper bound estimate of the efficiency of the NI. Results For the highest radio cell capacity, the network capacity valuesfor Spectral clustering, Agglomerative clustering and MOSAIC groupingsystem are 207234, 148056 and 87584 respectively. For every radio cellcapacity value, the mobility based clusters resulted in a higher network efficiency values than the MOSAIC. However, both spectral and agglomerativealgorithms have generated a very low quality clusters with the silhouettescores of 0.0717 and 0.0543 respectively. Conclusions Based on the analysis of the results, it can be concluded that,mobility based grouping of subscribers in the cellular network provide highernetwork efficiency values compared to the standard subscriber grouping systems such as MOSAIC.

Abstract This dissertation aimed at developing a multi-modal sensing u-healthcare system (MSUS), which reflects the unique properties of a healthcare application in a wireless sensor network. Together with health parameters, such as ECG, SpO2 and blood pressure, the system also transfers context-aware data, including activity, position and tracking data, in a wireless sensor network environment at home or in a hospital. Since packet loss may have fatal consequences for patients, health-related data are more critical than most other types of monitoring data. Thus, compared to environmental, agricultural or industrial monitoring, healthcare monitoring in a wireless environment imposes different requirements and priorities. These include heavy data traffic with wavelike parameters in wireless sensor network and fatal data loss due to the traffic. To ensure reliable data transfer in a wireless sensor network, this research placed special emphasis on the optimization of sampling rate, packet length and transmission rate, and on the traffic reduction method. To improve the reliability and accuracy of diagnosis, the u-healthcare system also collects context-aware information on the user’s activity and location and provides real-time tracking. Waveform health parameters, such as ECG, are normally sampled in the 100 to 400 Hz range according to the monitoring purpose. This type of waveform data may incur a heavy burden in wireless communication. To reduce wireless traffic between the sensor nodes and the gateway node, the system utilizes on-site ECG analysis implemented on the sensor nodes as well as query architecture. A 3D VRML viewer was also developed for the realistic monitoring of the user’s moving path and location. Two communication methods, an 802.15.4-based wireless sensor network and a CDMA cellular network are used by sensors placed on the users’ bodies to gather medical data, which is then transmitted to a server PC at home or in the hospital, depending on whether the sensor is within or outside the range of the wireless sensor network.

The positioning performance with the LVC (Last Visited Cells) report is evaluated and compared with extended reports with signal strength data. The LVC report contains cell identities and time spent in the last cells listened to. This is an off-line data source and the purpose of the positioning is to extract information about users’ whereabouts, which for example can be used to optimize the cellular network or vehicular traffic. The positioning evaluation is done in Matlab with a log-distance model, a fingerprinting algorithm, and a new LVC specific algorithm. A particle filter and a particle smoother is used to process simulated LVC reports and extended reports with different amount of information. The results are compared and evaluated with regard to the positioning accuracy and the information density of the reports.

The exponential increase of mobile users and the demand for high-speed data services has resulted in significant congestions in cellular backhaul capacity. As a solution to satisfy the traffic requirements of the existing 4G network, the 5G network has emerged as an enabling technology and a fundamental building block of next-generation communication networks. An essential requirement in 5G backhaul networks is their unparalleled capacity to handle heavy traffic between a large number of devices and the core network. Microwave and optic fiber technologies have been considered as feasible solutions for next-generation backhaul networks. However, such technologies are not cost effective to deploy, especially for the backhaul in high-density urban or rugged areas, such as those surrounded by mountains and solid rocks. Additionally, microwave technology faces alarmingly challenging issues, including limited data rates, scarcity of licensed spectrum, advanced interference management, and rough weather conditions (i.e., rain, which is the main weather condition that affects microwave signals the most). The focus of this work is to investigate the feasibility of using free-space-optical (FSO) technology in the 5G cellular backhaul network. FSO is a cost-effective and wide-bandwidth solution as compared to traditional backhaul solutions. However, FSO links are sensitive to atmospheric turbulence-induced fading, path loss, and pointing errors. Increasing the reliability of FSO systems while still exploiting their high data rate communications is a key requirement in the deployment of an FSO backhaul network. Overall, the theoretical models proposed in this work will be shown to enhance FSO link performance. In the experimental direction, we begin by designing an integrated mobile FSO system. To the best of our knowledge, no work in the literature has addressed the atmospheric path loss characterization of mobile FSO channels in a coastal environment. Therefore, we investigate the impact of weather effects in Thuwal, Saudi Arabia, over FSO links using outdoor and indoor setups. For the indoor experiments, results are reported based on a glass climate chamber in which we could precisely control the temperature and humidity.