A Computação Ubíqua (UbiComp) foi introduzida na década de 90 como a área de pesquisa que estuda a integração da tecnologia às tarefas cotidianas, com a intenção de proporcionar aos usuários a manutenção do foco em suas atividades, reduzindo a necessidade de seu envolvimento com a gerência da infraestrutura computacional. Considerando esta perspectiva de integração das tecnologias, exigindo o mínimo de participação do usuário, um dos desafios centrais de pesquisa para contemplar essa proposta da UbiComp é a Ciência de Situação. A construção do suporte à Ciência de Situação para as aplicações ubíquas envolve a representação do contexto, a coleta, o armazenamento e o processamento dos dados contextuais, bem como a identificação e disseminação das situações, de forma autônoma. Dentre as maneiras que tem sido consideradas para materializar a UbiComp, vem se destacando a Internet das Coisas (Internet of Things - IoT). Esta abordagem utiliza a Internet como principal meio para interoperação entre dispositivos computacionais. Nesse sentido, a IoT tem como premissa unir o mundo físico ao digital, preconizando a ideia do “tudo conectado”, criando assim uma rede de objetos incorporados ao ambiente de forma ubíqua. Deste modo, esta tese tem como objetivo conceber uma arquitetura para provimento de Ciência de Situação às aplicações ubíquas, na perspectiva da infraestrutura da Internet das Coisas. A arquitetura, denominada SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), é concebida considerando os trabalhos previamente desenvolvidos pelo grupo de pesquisa GPPD/UFRGS, particularmente o middleware EXEHDA (Execution Environment for Highly Distributed Applications) Entende-se como contribuições centrais desta tese: (i) a definição de uma abordagem híbrida para representação e processamento do contexto, visando à identificação de situações de interesse das aplicações; e (ii) a concepção de uma arquitetura distribuída, baseada em eventos e regras, visando o suporte à Ciência de Situação das aplicações ubíquas, considerando a infraestrutura provida pela IoT. As funcionalidades da Arquitetura SAUI são avaliadas através de cenários de uso nas áreas de agropecuária e saúde, sendo caracterizados os protótipos desenvolvidos, as tecnologias empregadas e os testes realizados. Os resultados obtidos corroboram com a abordagem da Arquitetura SAUI de considerar um suporte concomitante para operação distribuída, tratamento autônomo dos dados contextuais baseado em eventos e regras, e processamento híbrido do contexto, visando à identificação de situações de interesse das aplicações. / The Ubiquitous Computing (UbiComp) was introduced in the 90s as the area of research that studies the integration of technology in the everyday tasks, with the objective to keep the focus of the users in their activities, reducing the need of their involvement in the management of the computational infrastructure. Considering this perspective of technologies integration, requiring minimal user participation, one of the central research challenges is the situation awareness. The construction of the support to situation awareness for ubiquitous applications involves the representation of context; the acquisition, storage and processing of contextual data; and the identification and dissemination of situations, in autonomous way. Among the ways that have been considered to materialize UbiComp, has been highlighting the IoT (Internet of Things). This approach uses the Internet as the main means for interoperation between computing devices. In this sense, the IoT has the premise of join the physical and digital worlds, contemplating the idea of “everything connected”, thus creating a network of objects embedded in the environment ubiquitously. Thus, this thesis aims to design an architecture for providing situation awareness to ubiquitous applications, considering the IoT infrastructure. The architecture, called SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), is designed considering the work previously developed by the research group GPPD/UFRGS, particularly EXEHDA middleware (Execution Environment for Highly Distributed Applications) It is considered as central contributions of this thesis: (i) the definition of a hybrid approach for modeling and processing of context, aiming at identification of situations of interest of the applications; and (ii) the design of a distributed architecture, driven by events and rules, aiming at supporting the situation awareness of ubiquitous applications, considering an IoT infrastructure. The funcionalities of SAUI architecture are evaluated through usage scenarios in the areas of agriculture and healthcare. It was characterized the developed prototypes, the technologies used, and the tests performed. The results corroborate with the approach of SAUI architecture, which considers a concomitant support for distributed operation, autonomous handling of context based on events and rules, and hybrid processing of context, aiming at identification of situations.

The fifth generation (5G) wireless communications technology will be a paradigm shift which does not only provide an explosive increment on the achievable data rate per cell, but also ideally decreases the costs and energy consumption per data link. The engineering requirements of 5G standard can be intuitively interpreted as highly enhanced spectral efficiency and energy efficiency. This thesis focuses on the practical implementation issues of the massive multiple-input multiple-output (MIMO) and energy harvesting systems. To begin with, massive MIMO, as one of the key technologies of 5G systems, can provide enormous enhancement in spectral efficiency. For a practical massive MIMO system, hybrid processing (precoding/combining), by restricting the number of RF chains to far less than the number of antenna elements, can significantly reduce the implementation cost compared to the full-complexity radio frequency (RF) chain configuration. This thesis designs the hybrid RF and baseband precoders/combiners for multi-stream transmission in the point-to-point (P2P) massive MIMO systems, by directly decomposing the pre-designed digital precoder/combiner of a large dimension. The performance of the matrix decomposition based hybrid processing (MD-HP) scheme is near-optimal compared to the singular value decomposition (SVD) based full-complexity processing. In addition, the downlink communication of a massive multiuser MIMO (MU-MIMO) system is also investigated, and a low-complexity hybrid block diagonalization (Hy-BD) scheme is developed to approach the performance of the traditional BD method. We aim to harvest the large array gain through the phase-only RF precoding and combining and then BD processing is performed on the equivalent baseband channel in the massive MU-MIMO scenario. The MD-HP and Hy-BD schemes are examined in both the large Rayleigh fading channels and millimeter wave channels. On the other hand, energy harvesting is an increasingly attractive and renewable source of power for wireless communications devices, which contributes to the enhancement of the system energy efficiency. This thesis also designs the energy cooperation assisted energy harvesting communication between a practical transmitter and receiver, whose hardware circuits consume non-zero power when active. The energy cooperation save-then-transmit (EC-ST) scheme aims to obtain the optimal active time ratio and energy cooperation power for the maximum throughput under additive white Gaussian channels and the minimum outage probability under block Rayleigh fading channels. / Graduate

A Computação Ubíqua (UbiComp) foi introduzida na década de 90 como a área de pesquisa que estuda a integração da tecnologia às tarefas cotidianas, com a intenção de proporcionar aos usuários a manutenção do foco em suas atividades, reduzindo a necessidade de seu envolvimento com a gerência da infraestrutura computacional. Considerando esta perspectiva de integração das tecnologias, exigindo o mínimo de participação do usuário, um dos desafios centrais de pesquisa para contemplar essa proposta da UbiComp é a Ciência de Situação. A construção do suporte à Ciência de Situação para as aplicações ubíquas envolve a representação do contexto, a coleta, o armazenamento e o processamento dos dados contextuais, bem como a identificação e disseminação das situações, de forma autônoma. Dentre as maneiras que tem sido consideradas para materializar a UbiComp, vem se destacando a Internet das Coisas (Internet of Things - IoT). Esta abordagem utiliza a Internet como principal meio para interoperação entre dispositivos computacionais. Nesse sentido, a IoT tem como premissa unir o mundo físico ao digital, preconizando a ideia do “tudo conectado”, criando assim uma rede de objetos incorporados ao ambiente de forma ubíqua. Deste modo, esta tese tem como objetivo conceber uma arquitetura para provimento de Ciência de Situação às aplicações ubíquas, na perspectiva da infraestrutura da Internet das Coisas. A arquitetura, denominada SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), é concebida considerando os trabalhos previamente desenvolvidos pelo grupo de pesquisa GPPD/UFRGS, particularmente o middleware EXEHDA (Execution Environment for Highly Distributed Applications) Entende-se como contribuições centrais desta tese: (i) a definição de uma abordagem híbrida para representação e processamento do contexto, visando à identificação de situações de interesse das aplicações; e (ii) a concepção de uma arquitetura distribuída, baseada em eventos e regras, visando o suporte à Ciência de Situação das aplicações ubíquas, considerando a infraestrutura provida pela IoT. As funcionalidades da Arquitetura SAUI são avaliadas através de cenários de uso nas áreas de agropecuária e saúde, sendo caracterizados os protótipos desenvolvidos, as tecnologias empregadas e os testes realizados. Os resultados obtidos corroboram com a abordagem da Arquitetura SAUI de considerar um suporte concomitante para operação distribuída, tratamento autônomo dos dados contextuais baseado em eventos e regras, e processamento híbrido do contexto, visando à identificação de situações de interesse das aplicações. / The Ubiquitous Computing (UbiComp) was introduced in the 90s as the area of research that studies the integration of technology in the everyday tasks, with the objective to keep the focus of the users in their activities, reducing the need of their involvement in the management of the computational infrastructure. Considering this perspective of technologies integration, requiring minimal user participation, one of the central research challenges is the situation awareness. The construction of the support to situation awareness for ubiquitous applications involves the representation of context; the acquisition, storage and processing of contextual data; and the identification and dissemination of situations, in autonomous way. Among the ways that have been considered to materialize UbiComp, has been highlighting the IoT (Internet of Things). This approach uses the Internet as the main means for interoperation between computing devices. In this sense, the IoT has the premise of join the physical and digital worlds, contemplating the idea of “everything connected”, thus creating a network of objects embedded in the environment ubiquitously. Thus, this thesis aims to design an architecture for providing situation awareness to ubiquitous applications, considering the IoT infrastructure. The architecture, called SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), is designed considering the work previously developed by the research group GPPD/UFRGS, particularly EXEHDA middleware (Execution Environment for Highly Distributed Applications) It is considered as central contributions of this thesis: (i) the definition of a hybrid approach for modeling and processing of context, aiming at identification of situations of interest of the applications; and (ii) the design of a distributed architecture, driven by events and rules, aiming at supporting the situation awareness of ubiquitous applications, considering an IoT infrastructure. The funcionalities of SAUI architecture are evaluated through usage scenarios in the areas of agriculture and healthcare. It was characterized the developed prototypes, the technologies used, and the tests performed. The results corroborate with the approach of SAUI architecture, which considers a concomitant support for distributed operation, autonomous handling of context based on events and rules, and hybrid processing of context, aiming at identification of situations.

A Computação Ubíqua (UbiComp) foi introduzida na década de 90 como a área de pesquisa que estuda a integração da tecnologia às tarefas cotidianas, com a intenção de proporcionar aos usuários a manutenção do foco em suas atividades, reduzindo a necessidade de seu envolvimento com a gerência da infraestrutura computacional. Considerando esta perspectiva de integração das tecnologias, exigindo o mínimo de participação do usuário, um dos desafios centrais de pesquisa para contemplar essa proposta da UbiComp é a Ciência de Situação. A construção do suporte à Ciência de Situação para as aplicações ubíquas envolve a representação do contexto, a coleta, o armazenamento e o processamento dos dados contextuais, bem como a identificação e disseminação das situações, de forma autônoma. Dentre as maneiras que tem sido consideradas para materializar a UbiComp, vem se destacando a Internet das Coisas (Internet of Things - IoT). Esta abordagem utiliza a Internet como principal meio para interoperação entre dispositivos computacionais. Nesse sentido, a IoT tem como premissa unir o mundo físico ao digital, preconizando a ideia do “tudo conectado”, criando assim uma rede de objetos incorporados ao ambiente de forma ubíqua. Deste modo, esta tese tem como objetivo conceber uma arquitetura para provimento de Ciência de Situação às aplicações ubíquas, na perspectiva da infraestrutura da Internet das Coisas. A arquitetura, denominada SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), é concebida considerando os trabalhos previamente desenvolvidos pelo grupo de pesquisa GPPD/UFRGS, particularmente o middleware EXEHDA (Execution Environment for Highly Distributed Applications) Entende-se como contribuições centrais desta tese: (i) a definição de uma abordagem híbrida para representação e processamento do contexto, visando à identificação de situações de interesse das aplicações; e (ii) a concepção de uma arquitetura distribuída, baseada em eventos e regras, visando o suporte à Ciência de Situação das aplicações ubíquas, considerando a infraestrutura provida pela IoT. As funcionalidades da Arquitetura SAUI são avaliadas através de cenários de uso nas áreas de agropecuária e saúde, sendo caracterizados os protótipos desenvolvidos, as tecnologias empregadas e os testes realizados. Os resultados obtidos corroboram com a abordagem da Arquitetura SAUI de considerar um suporte concomitante para operação distribuída, tratamento autônomo dos dados contextuais baseado em eventos e regras, e processamento híbrido do contexto, visando à identificação de situações de interesse das aplicações. / The Ubiquitous Computing (UbiComp) was introduced in the 90s as the area of research that studies the integration of technology in the everyday tasks, with the objective to keep the focus of the users in their activities, reducing the need of their involvement in the management of the computational infrastructure. Considering this perspective of technologies integration, requiring minimal user participation, one of the central research challenges is the situation awareness. The construction of the support to situation awareness for ubiquitous applications involves the representation of context; the acquisition, storage and processing of contextual data; and the identification and dissemination of situations, in autonomous way. Among the ways that have been considered to materialize UbiComp, has been highlighting the IoT (Internet of Things). This approach uses the Internet as the main means for interoperation between computing devices. In this sense, the IoT has the premise of join the physical and digital worlds, contemplating the idea of “everything connected”, thus creating a network of objects embedded in the environment ubiquitously. Thus, this thesis aims to design an architecture for providing situation awareness to ubiquitous applications, considering the IoT infrastructure. The architecture, called SAUI (Situation-aware Architecture for Ubiquitous applications in the Internet of things), is designed considering the work previously developed by the research group GPPD/UFRGS, particularly EXEHDA middleware (Execution Environment for Highly Distributed Applications) It is considered as central contributions of this thesis: (i) the definition of a hybrid approach for modeling and processing of context, aiming at identification of situations of interest of the applications; and (ii) the design of a distributed architecture, driven by events and rules, aiming at supporting the situation awareness of ubiquitous applications, considering an IoT infrastructure. The funcionalities of SAUI architecture are evaluated through usage scenarios in the areas of agriculture and healthcare. It was characterized the developed prototypes, the technologies used, and the tests performed. The results corroborate with the approach of SAUI architecture, which considers a concomitant support for distributed operation, autonomous handling of context based on events and rules, and hybrid processing of context, aiming at identification of situations.

To meet the urgent demand of high-speed data rate and to support large number of users, the massive multiple-input multiple-output (MIMO) technology is becoming one of the most promising candidates for the next generation wireless communications, namely the 5G. To realize the full potential of massive MIMO, it is necessary to have the channel state information (CSI) (partially) available at the transmitter. Hence, an efficient channel estimation is one of the key enablers and also critical challenges for 5G communications. Dealing with such problems, this dissertation investigates the design of efficient pilot-data transmission pattern and channel estimation in massive MIMO for both multipair relaying and peer-to-peer systems. Firstly, this dissertation proposes a pilot-data transmission overlay scheme for multipair MIMO relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data by exploiting the asymptotic orthogonality of massive MIMO channels. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results verify that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, a power allocation problem is formulated to properly adjust the transmission power of source data transmission and relay data forwarding which further improves the system performance. Additionally, this dissertation proposes and analyzes an efficient HD decode-and-forward (DF) scheme, named sum decode-and-forward (SDF), with the physical layer network coding (PNC) in the multipair massive MIMO two-way relaying system. As comparison, a joint decode-and-forward (JDF) scheme applied to the multipair massive MIMO relaying is also proposed and investigated. In the SDF scheme, a half number of pilots are saved compared to the JDF scheme which in turn increases the spectral efficiency of the system. Both the theoretical analyses and numerical results verifies such superiority of the SDF scheme. Further, the power efficiency of the proposed schemes is also investigated. Simulation results show that the signal transmission power can be rapidly reduced if the massive antenna arrays are equipped on the RS and the required data transmission power can further decrease if the training power is fixed. Finally, this dissertation investigates the general channel estimation problem in the massive MIMO system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the performance of the proposed channel estimation can approach that of full-chain estimations depending on the degree of channel spatial correlation and the number of RF chains which is verified by simulation results in terms of both mean square error (MSE) and spectral efficiency. Moreover, a covariance matching method is proposed to obtain channel correlation in practice and the simulation verifies its effectiveness by evaluating the spectral efficiency performance in parametric channel models. / Graduate / 0537 / 0544 / leyuanpan@gmail.com