A geração e o uso dos mapas de ambiente de rádio (REM - Radio Environment Map) em sistemas de comunicações sem fio vêm sendo alvo de pesquisas recentes na literatura científica. Dentre as possíveis aplicações, o REM fornece informações importantes para os processos de predição e otimização de cobertura em sistemas de comunicações sem fio, pois é baseado em medidas coletadas diretamente da rede. Neste contexto, a geração do REM depende do processamento das medidas e suas localizações para a construção dos mapas, por meio de predições espaciais. Entretanto, a incerteza de localização das medidas coletadas pode degradar a acurácia das predições de forma significativa e, consequentemente, impactar as posteriores decições baseadas no REM. Este trabalho aborda o problema de geração do REM de forma mais realística, formulando um modelo de predição espacial que introduz erros de localização no ambiente de rádio de um sistema de comunicação sem fio. As investigações mostram que os impactos provocados pela incerteza de localização na geração do REM são significativos, especialmente nas técnicas de estimação utilizadas para a aprendizagem de parâmetros do modelo de predição espacial. Com isso, uma técnica de predição espacial é proposta e utiliza ferramentas da área geoestatística para superar os efeitos negativos causados pela incerteza de localização nas medidas. Simulações computacionais são desenvolvidas para a avaliação de desempenho das principais técnicas de predição no contexto de geração do REM, considerando o problema da incerteza de localização. Os resultados de simulação da técnica proposta são promissores e mostram que levar em conta a distribuição estatística dos erros de localização pode resultar em predições com maior acurácia para a geração do REM. A influência de diferentes aspectos da modelagem do ambiente de rádio também é analisada e reforçam a ideia de que a aprendizagem de parâmetros do ambiente de rádio tem um papel importante na acurácia das predições espaciais, que são fundamentais para a geração confiável do REM. Finalmente, um estudo experimental do REM é realizado por meio de uma campanha de medidas, permitindo explorar o desempenho dos algoritmos de aprendizagem de parâmetros e predições desenvolvidos neste trabalho. / The generation and use of radio environment maps (REM) in wireless systems has been the subject of recent research in the scientific literature. Among the possible applications, the REM provides important information for the coverage predicfition and optimization processes in wireless systems, since it is based on measurements collected directly on the network. In this context, the REM generation process depends on the processing of the measurements and their locations for the construction of the maps through spatial predictions. However, the location uncertainty related to the measurements collected can signicantly degrade the accuracy of the spatial predictions and, consequently, impact the decisions based on REM. This work addresses the problem of the REM generation in a more realistic way, through the formulation of a spatial prediction model that introduces location errors in the radio environment of a wireless communication system. The investigations show that the impacts of the location uncertainty on the REM generation are significant, especially in the estimation techniques used to learn the parameters of the spatial prediction model. Thus, a spatial prediction technique is proposed, based on geostatistical tools, to overcome the negative effects caused by the location uncertainty of the REM measurements. Computational simulations are developed for the performance evaluation of the main prediction techniques in the context of REM generation, considering the problem of location uncertainty. The simulation results of the proposed technique are promising and show that taking into account the statistical distribution of location errors can result in more accurate predictions for the REM generation process. The influence of different aspects of the radio environment modeling is also analyzed and reinforce the idea that the learning of radio environment parameters plays an important role in the accuracy of spatial predictions, which are fundamental for the reliable REM generation. Finally, an experimental study is carried out through a measurement campaign with the purpose of generating the REM in practice and to explore the performance of the learning and prediction algorithms developed in this work.

In recent years, cognitive radios and cognitive wireless networks have been introduced as a new paradigm for enabling much higher spectrum utilization, providing more reliable and personal radio services, reducing harmful interference, and facilitating the interoperability or convergence of different wireless communication networks. Cognitive radios are goal-oriented, autonomously learn from experience and adapt to changing operating conditions. Cognitive radios have the potential to drive the next generation of radio devices and wireless communication system design and to enable a variety of niche applications in demanding environments, such as spectrum-sharing networks, public safety, natural disasters, civil emergencies, and military operations. This research first introduces an innovative approach to developing cognitive radios based on the Radio Environment Map (REM). The REM can be viewed as an integrated database that provides multi-domain environmental information and prior knowledge for cognitive radios, such as the geographical features, available services and networks, spectral regulations, locations and activities of neighboring radios, policies of the users and/or service providers, and past experience. The REM, serving as a vehicle of network support to cognitive radios, can be exploited by the cognitive engine for most cognitive functionalities, such as situation awareness, reasoning, learning, planning, and decision support. This research examines the role of the REM in cognitive radio development from a network point of view, and focuses on addressing three specific issues about the REM: how to design and populate the REM; how to exploit the REM with the cognitive engine algorithms; and how to evaluate the performance of the cognitive radios. Applications of the REM to wireless local area networks (WLAN) and wireless regional area networks (WRAN) are investigated, especially from the perspectives of interference management and radio resource management, which illustrate the significance of cognitive radios to the evolution of wireless communications and the revolution in spectral regulation. Network architecture for REM-enabled cognitive radios and framework for REM-enabled situation-aware cognitive engine learning algorithms have been proposed and formalized. As an example, the REM, including the data model and basic application programmer interfaces (API) to the cognitive engine, has been developed for cognitive WRAN systems. Furthermore, REM-enabled cognitive cooperative learning (REM-CCL) and REM-enabled case- and knowledge-based learning algorithms (REM-CKL) have been proposed and validated with link-level or network-level simulations and a WRAN base station cognitive engine testbed. Simulation results demonstrate that the WRAN CE can adapt orders of magnitude faster when using the REM-CKL than when using the genetic algorithms and achieve near-optimal global utility by leveraging the REM-CKL and a local search. Simulation results also suggest that exploiting the Global REM information can considerably improve the performance of both primary and secondary users and mitigate the hidden node (or hidden receiver) problem. REM dissemination schemes and the resulting overhead have been investigated and analyzed under various network scenarios. By extending the optimized link state routing protocol, the overhead of REM dissemination in wireless ad hoc networks via multipoint relays can be significantly reduced by orders of magnitude as compared to plain flooding. Performance metrics for various cognitive radio applications are also proposed. REM-based scenario-driven testing (REM-SDT) has been proposed and employed to evaluate the performances of the cognitive engine and cognitive wireless networks. This research shows that REM is a viable, cost-efficient approach to developing cognitive radios and cognitive wireless networks with significant potential in various applications. Future research recommendations are provided in the conclusion. / Ph. D.

[pt] Este trabalho analisa um problema específico envolvendo o compartilhamento de frequências entre o Serviço Fixo Terrestre (FS) e uma aplicação de alta densidade do Serviço Fixo por Satélite (HDFSS). Este problema, identificado pela primeira vez durante as discussões sobre sistemas de alta densidade ocorridas durante a Conferência Mundial de Radiocomunicações da União Internacional de Telecomunicações de 2003 tem, desde então, sido objeto de diversos estudos. No cenário geral considerado neste trabalho, sistemas do FS e do HDFSS compartilham a mesma faixa de frequências em uma mesma região geográfica. Os sistemas do FS operam como usuários primários e os sistemas HDFSS operam, utilizando técnicas de Rádio Cognitivo, como usuários secundários. Para facilitar a convivência entre os dois serviços, o uso de um Mapa de Ambiente de Rádio (REM - Radio Environment Map) é considerado. Este tipo de mapa, produzido por um Centro de Base de Dados (Data Base Center) a partir de informações recebidas dos usuários secundários, indica em que partes da região geográfica considerada um determinado canal está disponível para uso pelos usuários secundários. Neste trabalho, um novo método de geração de REM é proposto e avaliado. No desenvolvimento do método é utilizada uma modelagem original que considera uma densidade espectral de potência genérica para os sinais transmitidos pelos usuários primários. Além disso, métricas adequadas para avaliar a qualidade de REMs e o desempenho dos métodos de geração são definidas e propostas. Estas métricas são utilizadas numa análise comparativa de desempenho envolvendo o método de geração de REM proposto e um método de geração existente. A análise é feita com base nos resultados obtidos para cenários específicos que, diferentemente dos cenários utilizados em outros trabalhos, considera a existência de múltiplos enlaces do FS na região de interesse. / [en] This work analyzes a specific problem involving the frequency sharing between the Fixed Service (FS) and a High Density application in the Fixed Satellite Service (HDFSS). This problem was first identified through discussions on high density systems held at the 2003 World Radiocommunication Conference and, since then, it has been the object of several studies. This study considers a general scenario involving a geographical area where FS systems shares its operating frequency band with HDFSS systems. The FS systems operate as primary users and the HDFSS systems operate as secondary users using Cognitive Radio techniques. To facilitate the frequency sharing between these two systems, the use of a Radio Environment Map (REM) is considered. This kind of map, produced by a Data Base Center (DBC), is based on the collaboration data received from all secondary users and indicates those locations (inside the considered geographical area) where a given frequency channel is available for use by secondary users. In this work, a new REM generation method is proposed and evaluated. In developing the method, an original mathematical modeling, that considers a generic power spectrum density for the primary users transmissions, is used. Furthermore, appropriate metrics are defined and proposed aiming to assess the REM quality and the REM generation method performance. These metrics are used in a comparative performance analysis involving the proposed REM generation method and an existing generation method. This analysis is based on results obtained for specific scenarios that, unlike those evaluated in other studies, consider the existence of multiple FS links in the geographical area of interest.