In the last decade, the world has witnessed rapid increasing applications of wireless networks. However, with the fixed spectrum allocation policy that has been used since the beginning of the spectrum regulation to assign different spectrum bands to different wireless applications, it has been observed that most of the allocated spectrum bands are underutilized. Therefore, if these bands can be opportunistically used by new emerging wireless networks, the spectrum scarcity can be resolved. Cognitive Radio (CR) is a revolutionary and promising technology that can identify and then exploit the spectrum opportunities. In Cognitive Radio Networks (CRNs), the spectrum can be utilized by two kinds of users: Primary Users (PUs) having exclusive licenses to use certain spectrum bands for specific wireless applications, and Secondary Users (SUs) having no spectrum licenses but seeking for any spectrum opportunities. The SUs can make use of the licensed unused spectrum if they do not make any harmful interference to the PUs. However, the variation of the spectrum availability over the time and locations, due to the coexistence with the PUs, and the spread of the spectrum opportunities over wide spectrum bands create a unique trait of the CRNs. This key trait poses great challenges in different aspects of the radio resource management in CRNs such as the spectrum sensing, spectrum access, admission control, channel allocation, Quality-of-Service (QoS) provisioning, etc. In this thesis, we study the resource management of both single-hop and multi-hop CRNs. Since most of the new challenges in CRNs can be tackled by designing an efficient Medium Access Control (MAC) framework, where the solutions of these challenges can be integrated for efficient resource management, we firstly propose a novel MAC framework that integrates a kind of cooperative spectrum sensing method at the physical layer into a cooperative MAC protocol considering the requirements of both the SUs and PUs. For spectrum identification, a computationally simple but efficient sensing algorithm is developed, based on an innovative deterministic sensing policy, to assist each sensing user for identifying the optimum number of channels to sense and the optimum sensing duration. We then develop an admission control scheme and channel allocation policy that can be integrated in the proposed MAC framework to regulate the number of sensing users and number of access users; therefore, the spectrum identification and exploitation can be efficiently balanced. Moreover, we propose a QoS-based spectrum allocation framework that jointly considers the QoS provisioning for heterogeneous secondary Real-Time (RT) and Non-Real Time (NRT) users with the spectrum sensing, spectrum access decision, and call admission control. We analyze the proposed QoS-based spectrum allocation framework and find the optimum numbers of the RT and NRT users that the network can support. Finally, we introduce an innovative user clustering scheme to efficiently manage the spectrum identification and exploitation in multi-hop ad hoc CRNs. We group the SUs into clusters based on their geographical locations and occurring times and use spread spectrum techniques to facilitate using one frequency for the Common Control Channels (CCCs) of the whole secondary network and to reduce the co-channel interference between adjacent clusters by assigning different spreading codes for different clusters. The research results presented in this thesis contribute to realize the concept of the CRNs by developing a practical MAC framework, spectrum sensing, spectrum allocation, user admission control, and QoS provisioning for efficient resource management in these promising networks.

<p>The traditional way of increasing capacity in a wirelesscommunication system has been cell splitting and fixedchannel-allocation based on prediction tools. However, theplanning complexity increases rapidly with the number of cellsand the method is not suitable for the large temporal andspatial traffic variations expected in the future. A lot ofresearch has therefore been performed regarding adaptivechannel allocation, where a channel can be used anywhere aslong as the signal-to-interference ratio (SIR) is acceptable. Acommon opinion is that these solutions must be decentralizedsince a centralized one would be overly complex.</p><p>In this thesis, we study the locally centralized<i>bunch concept</i>for radio resource management (RRM) in aManhattan environment and show that it can give a very highcapacity both for outdoor users and for indoor users covered byoutdoor base stations. We show how measurement limitations anderrors affect the performance and wepropose methods to handlethese problems, e.g. averaging of measured values, robustchannel selection algorithms, and increased SIR margins. Wealso study the computational and signaling complexities andshow that they can be reduced by splitting large bunches, usingsparse matrix calculations, and by using a simplified admissionalgorithm. However, a reduction of the complexity often means areduction of the system capacity.</p><p>The measurements needed for RRM can also be used to find amobile terminal's geographical position. We propose and studysome simple yet accurate methods for this purpose. We alsostudy if position information can enhance RRM as is oftensuggested in the literature. In the studied scenario, thisinformation seems to be of limited use. One possible use is toestimate the mobile user's speed, to assist handover decisions.Another use is to find the location of user hotspots in anarea, which is beneficial for system planning.</p><p>Our results show that the bunch concept is a promisingcandidate for radio resource management in future wirelesssystems. We believe that the complexity is manageable and themain price we have to pay for high capacity is frequentreallocation of connections.</p><p><b>Keywords:</b>bunch concept, radio resource management,network-assisted resource management, base station selection,dynamic channel allocation, DCA, channel selection,least-interfered, interference avoidance, interferenceaveraging, handover, power control, path-loss measurements,signal strength, link-gain matrix, TD-CDMA, UTRA TDD, Manhattanscenario, microcells, mobile positioning, position accuracy,trilateration, triangulation, speed estimation</p>

In the last decade, the world has witnessed rapid increasing applications of wireless networks. However, with the fixed spectrum allocation policy that has been used since the beginning of the spectrum regulation to assign different spectrum bands to different wireless applications, it has been observed that most of the allocated spectrum bands are underutilized. Therefore, if these bands can be opportunistically used by new emerging wireless networks, the spectrum scarcity can be resolved. Cognitive Radio (CR) is a revolutionary and promising technology that can identify and then exploit the spectrum opportunities. In Cognitive Radio Networks (CRNs), the spectrum can be utilized by two kinds of users: Primary Users (PUs) having exclusive licenses to use certain spectrum bands for specific wireless applications, and Secondary Users (SUs) having no spectrum licenses but seeking for any spectrum opportunities. The SUs can make use of the licensed unused spectrum if they do not make any harmful interference to the PUs. However, the variation of the spectrum availability over the time and locations, due to the coexistence with the PUs, and the spread of the spectrum opportunities over wide spectrum bands create a unique trait of the CRNs. This key trait poses great challenges in different aspects of the radio resource management in CRNs such as the spectrum sensing, spectrum access, admission control, channel allocation, Quality-of-Service (QoS) provisioning, etc. In this thesis, we study the resource management of both single-hop and multi-hop CRNs. Since most of the new challenges in CRNs can be tackled by designing an efficient Medium Access Control (MAC) framework, where the solutions of these challenges can be integrated for efficient resource management, we firstly propose a novel MAC framework that integrates a kind of cooperative spectrum sensing method at the physical layer into a cooperative MAC protocol considering the requirements of both the SUs and PUs. For spectrum identification, a computationally simple but efficient sensing algorithm is developed, based on an innovative deterministic sensing policy, to assist each sensing user for identifying the optimum number of channels to sense and the optimum sensing duration. We then develop an admission control scheme and channel allocation policy that can be integrated in the proposed MAC framework to regulate the number of sensing users and number of access users; therefore, the spectrum identification and exploitation can be efficiently balanced. Moreover, we propose a QoS-based spectrum allocation framework that jointly considers the QoS provisioning for heterogeneous secondary Real-Time (RT) and Non-Real Time (NRT) users with the spectrum sensing, spectrum access decision, and call admission control. We analyze the proposed QoS-based spectrum allocation framework and find the optimum numbers of the RT and NRT users that the network can support. Finally, we introduce an innovative user clustering scheme to efficiently manage the spectrum identification and exploitation in multi-hop ad hoc CRNs. We group the SUs into clusters based on their geographical locations and occurring times and use spread spectrum techniques to facilitate using one frequency for the Common Control Channels (CCCs) of the whole secondary network and to reduce the co-channel interference between adjacent clusters by assigning different spreading codes for different clusters. The research results presented in this thesis contribute to realize the concept of the CRNs by developing a practical MAC framework, spectrum sensing, spectrum allocation, user admission control, and QoS provisioning for efficient resource management in these promising networks.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-08-24T17:59:29Z No. of bitstreams: 1 brunomarquescremonezi.pdf: 4447666 bytes, checksum: aa9f96e265197cfc5a70f8dfc8ad53ad (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-25T12:03:47Z (GMT) No. of bitstreams: 1 brunomarquescremonezi.pdf: 4447666 bytes, checksum: aa9f96e265197cfc5a70f8dfc8ad53ad (MD5) / Made available in DSpace on 2017-08-25T12:03:47Z (GMT). No. of bitstreams: 1 brunomarquescremonezi.pdf: 4447666 bytes, checksum: aa9f96e265197cfc5a70f8dfc8ad53ad (MD5) Previous issue date: 2017-06-02 / FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais / O aumento do uso de redes sem fios e a constante miniaturização dos dispositivos permitiram o desenvolvimento das redes de sensores corporais sem fio (do inglês, wireless body area networks — WBANs). Nessas redes, diversos sensores são posicionados sobre ou sob a pele do usuário. Os sensores de uma WBAN coletam dados sobre batimentos cardíacos, temperatura corporal ou até mesmo um prolongado cardiograma. Através do uso de WBAN, os usuários terão um monitoramento não invasivo e que pouco afeta a sua mobilidade. Essas características, no entanto, abrem portas para diversos problemas. Por transmitir informações críticas, a comunicação é sensível à latência e à perda de pacotes. De fato, alta latência e perda de dados vitais podem acarretar em sérias consequências na vida dos pacientes e, em casos extremos, levando ao óbito. As características inerentes em uma comunicação sem fio geram problemas para redes corporais. Com sua popularização e alta mobilidade, é razoável considerar a existência de ambientes médicos muito densos, em que duas ou mais redes corporais podem utilizar simultaneamente o mesmo canal de comunicação sem fio. Essa situação potencializa as interferências, acarretando um maior número de retransmissões e perdas de pacotes, e, consequentemente, levando a um aumento da latência. Diante disso, este trabalho apresenta o PDAC (Protocol for Dynamic Channel AlioCation), um protocolo para alocação dinâmica de canais, ciente dos requisitos de aplicações médicas. O PDAC oferece uma solução para reduzir interferências entre redes corporais sem fio tirando proveito da arquitetura de um ambiente hospitalar. No PDAC, diversas estações base trabalham de forma colaborativa para atender aos requisitos das aplicações médicas. Para uma alocação livre de interferências, o PDAC é inspirado por uma solução gulosa de um problema de coloração de grafos, oferecendo meios para evitar que estações base vizinhas utilizem o mesmo canal simultaneamente. Além disso, o PDAC oferece, através da agregação de canais, melhores vazões. A avaliação de desempenho do PDAC foi realizada em duas fases: por meio de experimentos de simulação e análises formais. Os resultados de simulação indicam que, em um ambiente médico realista, o PDAC é capaz de em média aumentar a vazão em 30% e reduzir a latência em 40%, quando comparado com protocolos de alocação de frequência do estado da arte. A outra fase consiste na verificação formal que por sua vez mostrou a coerência do protocolo e que o mesmo satisfaz todas as propriedades de segurança verificadas. / The increased use of wireless networks and the constant miniaturization of devices allowed the development of wireless body area networks (WBANs). In these networks, diverse sensors are positioned on the user's skin. The sensors in a WBAN gather data from heart rate, body temperature or even a cardiogram. Through the use of WBAN, patients will have a noninvasive monitoring system, which hardly affects their mobility. These characteristics, however, create several problems. By transmitting critical informa-tion, these data are quite sensitive to high latency and packet loss. The loss of vital data can lead to serious consequences in the users' life and, in extreme cases, leading to death. The inherent characteristics of wireless communication are a major issue for WBANs. With their popularization and high mobility, it is reasonable to consider the existence of very dense medical environments, where two or more WBANs can simultaneously use the same wireless communication channel. This situation can produce interference, leading to a bigger number of retransmissions and packet losses, and consequently increasing latency. Therefore, this master thesis presents the PDAC (Protocol for Dynamic Channel AlloCation), a protocol for dynamic channel allocation, that is aware of the requirements of medical applications. PDAC offers a solution to reduce interference between WBANs by taking advantage of the architecture of a hospital environment. Using PDAC, several base stations work collaboratively to meet medical application requirements. For an interference-free allocation, PDAC is inspired by a greedy solution of a graph colouring problem, preventing neighbouring base stations of using the same channel simultaneously. In addition, PDAC offers through the channel bonding, a better goodput. The evaluation PDAC was performed in two phases: by means of simulations and formal analysis. Simulation results indicate that PDAC is able to increase goodput by 30% (on average) and reduce latency by 40% (on average) when compared to the literature. The formal verification, in turn, shows that the protocol is consistent and also satisfies all verified security properties.

The heterogeneity and complexity of modern communication networks demands coupling network nodes with intelligence to perceive and adapt to different network conditions autonomously. Cognitive Networking is an emerging networking research area that aims to achieve this goal by applying distributed reasoning and learning across the protocol stack and throughout the network. Various cognitive node and cognitive network architectures with different levels of maturity have been proposed in the literature. All of them adopt the idea of coupling network devices with sensors to sense network conditions, artificial intelligence algorithms to solve problems, and a reconfigurable platform to apply solutions. However, little further research has investigated suitable reasoning and learning algorithms. In this dissertation, we take cognitive network research a step further by investigating the reasoning component of cognitive networks. In a deviation from previous suggestions, we suggest the use of a single flexible distributed reasoning algorithm for cognitive networks. We first propose an architecture for a cognitive node in a cognitive network that is general enough to apply to future networking challenges. We then introduce and justify our choice of the island genetic algorithm (iGA) as the distributed reasoning algorithm. Having introduced our cognitive node architecture, we then focus on the applicability of the island genetic algorithm as a single reasoning algorithm for cognitive networks. Our approach is to apply the island genetic algorithm to different single and cross layer communication and networking problems and to evaluate its performance through simulation. A proof of concept cognitive network is implemented to understand the implementation challenges and assess the island genetic algorithm performance in a real network environment. We apply the island genetic algorithm to three problems: channel allocation, joint power and channel allocation, and flow routing. The channel allocation problem is a major challenge for dynamic spectrum access which, in turn, has been the focal application for cognitive radios and cognitive networks. The other problems are examples of hard cross layer problems. We first apply the standard island genetic algorithm to a channel allocation problem formulated for the dynamic spectrum cognitive network environment. We also describe the details for implementing a cognitive network prototype using the universal software radio peripheral integrated with our extended implementation of the GNU radio software package and our island genetic algorithm implementation for the dynamic spectrum channel allocation problem. We then develop a localized variation of the island genetic algorithm, denoted LiGA, that allows the standard island genetic algorithm to scale and apply it to the joint power and channel allocation problem. In this context, we also investigate the importance of power control for cognitive networks and study the effect of non-cooperative behavior on the performance of the LiGA. The localized variation of the island genetic algorithm, LiGA, is powerful in solving node-centric problems and problems that requires only limited knowledge about network status. However, not every communication and networking problems can be solved efficiently in localized fashion. Thus, we propose a generalized version of the LiGA, namely the K-hop island genetic algorithm, as our final distributed reasoning algorithm proposal for cognitive networks. The K-hop island genetic algorithm is a promising algorithm to solve a large class of communication and networking problems with controllable cooperation and migration scope that allows for a tradeoff between performance and cost. We apply it to a flow routing problem that includes both power control and channel allocation. For all problems simulation results are provided to quantify the performance of the island genetic algorithm variation. In most cases, simulation and experimental results reveal promising performance for the island genetic algorithm. We conclude our work with a discussion of the shortcomings of island genetic algorithms without guidance from a learning mechanism and propose the incorporation of two learning processes into the cognitive node architecture to solve slow convergence and manual configuration problems. We suggest the cultural algorithm framework and reinforcement learning techniques as candidate leaning techniques for implementing the learning processes. However, further investigation and implementation is left as future work. / Ph. D.

The growing demand for ubiquitous broadband network connectivity and continuously falling prices in hardware operating on the unlicensed bands have put Wi-Fi technology in a position to lead the way in rapid innovation towards high performance wireless for the future. The success story of Wi-Fi contributed to the development of widespread variety of options for unlicensed access (e.g., Bluetooth, Zigbee) and has even sparked regulatory bodies in several countries to permit access to unlicensed devices in portions of the spectrum initially licensed to TV services. In this thesis we present novel spectrum management algorithms for networks employing 802.11 and TV white spaces broadly aimed at efficient use of spectrum under consideration, lower contention (interference) and high performance. One of the target scenarios of this thesis is neighbourhood or citywide wireless access. For this, we propose the use of IEEE 802.11-based multi-radio wireless mesh network using omnidirectional antennae. We develop a novel scalable protocol termed LCAP for efficient and adaptive distributed multi-radio channel allocation. In LCAP, nodes autonomously learn their channel allocation based on neighbourhood and channel usage information. This information is obtained via a novel neighbour discovery protocol, which is effective even when nodes do not share a common channel. Extensive simulation-based evaluation of LCAP relative to the state-of-the-art Asynchronous Distributed Colouring (ADC) protocol demonstrates that LCAP is able to achieve its stated objectives. These objectives include efficient channel utilisation across diverse traffic patterns, protocol scalability and adaptivity to factors such as external interference. Motivated by the non-stationary nature of the network scenario and the resulting difficulty of establishing convergence of LCAP, we consider a deterministic alternative. This approach employs a novel distributed priority-based mechanism where nodes decide on their channel allocations based on only local information. Key enabler of this approach is our neighbour discovery mechanism. We show via simulations that this mechanism exhibits similar performance to LCAP. Another application scenario considered in this thesis is broadband access to rural areas. For such scenarios, we consider the use of long-distance 802.11 mesh networks and present a novel mechanism to address the channel allocation problem in a traffic-aware manner. The proposed approach employs a multi-radio architecture using directional antennae. Under this architecture, we exploit the capability of the 802.11 hardware to use different channel widths and assign widths to links based on their relative traffic volume such that side-lobe interference is mitigated. We show that this problem is NP-complete and propose a polynomial time, greedy channel allocation algorithm that guarantees valid channel allocations for each node. Evaluation of the proposed algorithm via simulations of real network topologies shows that it consistently outperforms fixed width allocation due to its ability to adapt to spatio-temporal variations in traffic demands. Finally, we consider the use of TV-white-spaces to increase throughput for in-home wireless networking and relieve the already congested unlicensed bands. To the best of our knowledge, our work is the first to develop a scalable micro auctioning mechanism for sharing of TV white space spectrum through a geolocation database. The goal of our approach is to minimise contention among secondary users, while not interfering with primary users of TV white space spectrum (TV receivers and microphone users). It enables interference-free and dynamic sharing of TVWS among home networks with heterogeneous spectrum demands, while resulting in revenue generation for database and broadband providers. Using white space availability maps from the UK, we validate our approach in real rural, urban and dense-urban residential scenarios. Our results show that our mechanism is able to achieve its stated objectives of attractiveness to both the database provider and spectrum requesters, scalability and efficiency for dynamic spectrum distribution in an interference-free manner.

Conventional cellular systems have not taken full advantage of fractional frequency reuse and adaptive allocation due to the fixed cluster size and uniformed channel assignment procedures. This problem may cause more fatal consequences considering the cutting-edge 4G standards which have higher data rate requirements such as 3GPP-LTE and IEEE 802.16m (WiMAX). In this thesis, three different partitioning schemes for adaptive clustering with fractional frequency reuse were proposed and investigated. An overlaid cellular clustering scheme which uses adaptive fractional frequency reuse factors would provide a better end-user experience by exploiting the high level of signal to interference ratio (SIR). The proposed methods are studied via simulations and the results show that the adaptive clustering with different partitioning methods provide better capacity and grade of service (GoS) comparing to the conventional cellular architecture methodologies.

Fueled by the rapid advancement in digital and wireless technologies, the ever-increasing capabilities of wireless devices have placed upon us a tremendous challenge - how to put all of this capability to effective use. Individually, wireless devices have outpaced the ability of users to optimally configure them. Collectively, the complexity is far more daunting. Research in cognitive networks seeks to provide a solution to the diffculty of effectively using the expanding capabilities of wireless networks by embedding greater degrees of intelligence within the network itself. In this dissertation, we address some fundamental questions related to cognitive networks, such as "What is a cognitive network?" and "What methods may be used to design a cognitive network?" We relate cognitive networks to a common artificial intelligence (AI) framework, the multi-agent system (MAS). We also discuss the key elements of learning and reasoning, with the ability to learn being the primary differentiator for a cognitive network. Having discussed some of the fundamentals, we proceed to further illustrate the cognitive networking principle by applying it to two problems: multichannel topology control for dynamic spectrum access (DSA) and routing in a mobile ad hoc network (MANET). The multichannel topology control problem involves confguring secondary network parameters to minimize the probability that the secondary network will cause an outage to a primary user in the future. This requires the secondary network to estimate an outage potential map, essentially a spatial map of predicted primary user density, which must be learned using prior observations of spectral occupancy made by secondary nodes. Due to the complexity of the objective function, we provide a suboptimal heuristic and compare its performance against heuristics targeting power-based and interference-based topology control objectives. We also develop a genetic algorithm to provide reference solutions since obtaining optimal solutions is impractical. We show how our approach to this problem qualifies as a cognitive network. In presenting our second application, we address the role of network state observations in cognitive networking. Essentially, we need a way to quantify how much information is needed regarding the state of the network to achieve a desired level of performance. This question is applicable to networking in general, but becomes increasingly important in the cognitive network context because of the potential volume of information that may be desired for decision-making. In this case, the application is routing in MANETs. Current MANET routing protocols are largely adapted from routing algorithms developed for wired networks. Although optimal routing in wired networks is grounded in dynamic programming, the critical assumption, static link costs and states, that enables the use of dynamic programming for wired networks need not apply to MANETs. We present a link-level model of a MANET, which models the network as a stochastically varying graph that possesses the Markov property. We present the Markov decision process as the appropriate framework for computing optimal routing policies for such networks. We then proceed to analyze the relationship between optimal policy and link state information as a function of minimum distance from the forwarding node. The applications that we focus on are quite different, both in their models as well as their objectives. This difference is intentional and signficant because it disassociates the technology, i.e. cognitive networks, from the application of the technology. As a consequence, the versatility of the cognitive networks concept is demonstrated. Simultaneously, we are able to address two open problems and provide useful results, as well as new perspective, on both multichannel topology control and MANET routing. This material is posted here with permission from the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Virginia Tech library's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it. / Ph. D.

Nos últimos anos, as redes de comunicação móveis se tornaram de fundamental importância para a infraestrutura dos sistemas de comunicação. Uma das áreas de maior crescimento é a computação móvel. Realizada através de sinais de rádio, a quantidade de canais disponíveis raramente é suficiente para atender a crescente demanda. Este trabalho apresenta uma solução para a questão da alocação de canais, um tópico desafiador dentro da área de redes móveis. A implementação de alocação dinâmica com uso de técnicas computacionais clássicas melhora a utilização dos recursos disponíveis,mas necessita de ajustes periódicos para se adequar a novos cenários. Para a construção de um sistema mais flexível e adaptável, a abordagem escolhida utiliza técnicas de Inteligência Artificial. O modelo proposto combina Teoria Nebulosa, Redes Neurais Artificiais e Sistemas Multi-Agentes. As características de cada técnica foram analisadas e identificamos as partes do sistema que poderiam ser beneficiadas por cada uma. O sistema é resultado da combinação coordenada das três técnicas, e constitui um método eficiente e flexível para gerenciamento de recursos de rádio. Após o detalhamento do modelo, realizamos uma simulação de uma rede celular com o sistema proposto e seu comportamento é comparado com uma rede de referência, para verificação das diferenças e melhorias alcançadas. Por fim, apresentamos a situação atual da pesquisa e os possíveis caminhos para aprimoramento do sistema. / In the last years, mobile networks became more important for communication systems’ infrastructure. One area of great growth is mobile computation, which is performed through radio signals. The amount of available channels rarely is enough to attend the increasing demand. This work presents a solution for the channel allocation topic, a challenging topic inside mobile networks area. The implementation of dynamic allocation using classic computational techniques improves the use of available resources, but it needs periodic and frequent adjustments for new scenarios. The construction of a more flexible and adaptable system was achieved using Artificial Intelligence techniques. Proposed model combines Fuzzy Logic, Artificial Neural Networks and Multi-Agents Systems. Features of each technique had been analyzed and we identified the system modules which could be benefited by them. The system is the result of coordinated combination of these three techniques, and constitutes an efficient and flexible method for radio resources management. After model detailing, we executed a cellular network simulation using proposed system, and its behavior is compared with a reference network, presenting reached differences and improvements. Finally, we present current situation of this research and possible ways for system improvement.

Nos últimos anos, as redes de comunicação móveis se tornaram de fundamental importância para a infraestrutura dos sistemas de comunicação. Uma das áreas de maior crescimento é a computação móvel. Realizada através de sinais de rádio, a quantidade de canais disponíveis raramente é suficiente para atender a crescente demanda. Este trabalho apresenta uma solução para a questão da alocação de canais, um tópico desafiador dentro da área de redes móveis. A implementação de alocação dinâmica com uso de técnicas computacionais clássicas melhora a utilização dos recursos disponíveis,mas necessita de ajustes periódicos para se adequar a novos cenários. Para a construção de um sistema mais flexível e adaptável, a abordagem escolhida utiliza técnicas de Inteligência Artificial. O modelo proposto combina Teoria Nebulosa, Redes Neurais Artificiais e Sistemas Multi-Agentes. As características de cada técnica foram analisadas e identificamos as partes do sistema que poderiam ser beneficiadas por cada uma. O sistema é resultado da combinação coordenada das três técnicas, e constitui um método eficiente e flexível para gerenciamento de recursos de rádio. Após o detalhamento do modelo, realizamos uma simulação de uma rede celular com o sistema proposto e seu comportamento é comparado com uma rede de referência, para verificação das diferenças e melhorias alcançadas. Por fim, apresentamos a situação atual da pesquisa e os possíveis caminhos para aprimoramento do sistema. / In the last years, mobile networks became more important for communication systems’ infrastructure. One area of great growth is mobile computation, which is performed through radio signals. The amount of available channels rarely is enough to attend the increasing demand. This work presents a solution for the channel allocation topic, a challenging topic inside mobile networks area. The implementation of dynamic allocation using classic computational techniques improves the use of available resources, but it needs periodic and frequent adjustments for new scenarios. The construction of a more flexible and adaptable system was achieved using Artificial Intelligence techniques. Proposed model combines Fuzzy Logic, Artificial Neural Networks and Multi-Agents Systems. Features of each technique had been analyzed and we identified the system modules which could be benefited by them. The system is the result of coordinated combination of these three techniques, and constitutes an efficient and flexible method for radio resources management. After model detailing, we executed a cellular network simulation using proposed system, and its behavior is compared with a reference network, presenting reached differences and improvements. Finally, we present current situation of this research and possible ways for system improvement.