Cette thèse s’intéresse aux concepts de mobilité et de gestion du spectre dans les réseaux à radio cognitive. Ainsi, nous avons proposé deux approches décentralisées basées sur les systèmes multi-agents (SMA). Nous avons, tout d’abord, intégré des agents au sein des utilisateurs secondaires (n’ayant pas de licence pour l’accès au spectre) et des utilisateurs primaires (disposant d’une licence) et nous avons défini leurs comportements au moment du handover. Notre première solution NESAM propose un mécanisme de négociation entre les agents permettant aux utilisateurs secondaires de se voir allouer une bande de spectre avec un bon rapport prix par durée d’allocation. Nous avons, par ailleurs, proposé une deuxième solution LASMA qui se base sur l’enchère combinée avec de l’apprentissage pour assurer une gestion efficace du spectre ainsi qu’une gestion de la mobilité des utilisateurs à radio cognitive. Nos algorithmes prennent en compte les préférences des utilisateurs, comme la fréquence spectrale, le prix et la durée ainsi que les contraintes de l’environnement spectral telles que les bandes de fréquences disponibles. Nos propositions assurent une exploitation importante des ressources spectrales tout en diminuant le nombre de handovers spectraux. De plus, nos algorithmes offrent un handover spectral transparent et sans interruption lors des déplacements des utilisateurs. Nous avons prouvé également que nos solutions permettent de satisfaire les besoins des utilisateurs et d’améliorer leur utilité / In this thesis, we are interested in mobile cognitive radio networks while ensuring an efficient spectrum sharing and seamless handover at the same time. Hence, we propose two decentralized approaches based on multi-agents systems. We first deployed agents on each primary (licensed) and secondary (unlicensed cognitive radio) users, respectively. Besides, we define agents’ behaviors during the handover process.Our proposal NESAM defines a novel negotiation mechanism between agents to allow secondary users assigning the appropriate spectrum band giving a good price for the use duration. We have also proposed a second solution LASMA using the learning based auctions. Our algorithms take into account users’ requirements such as spectrum frequency, price and duration as well as environment’s constraints such as available resources.Our proposals improve the overall spectrum utilization and minimize the number of spectrum handovers when users move from one network to another one. This proves that our algorithms ensure efficient spectrum allocation and enable seamless handover during user’s mobility. Besides, we proved that our approaches guarantee users’ satisfaction and improve their utility

Cognitive radios are the radios which use spectrum licensed to other users. For this, they perform Radio Environment Analysis, identify the Spectral holes and then operate in those holes. We consider the problem of Spectrum Sensing in Cognitive Radio Networks. Our Algorithms are based on Sequential Change Detection techniques. In this work we have used DualCUSUM, a distributed algorithm developed recently for cooperative spectrum sensing. This is used by cognitive (secondary) nodes to sense the spectrum which then send their local decisions to a fusion center. The fusion center again sequentially processes the received information to arrive at the final decision. We show that DualCUSUM performs better than all other existing spectrum sensing algorithms. We present a generalized analysis of DualCUSUM and compare the analysis with simulations to show its accuracy. DualCUSUM requires the knowledge of the channel gains for each of the secondary users and the receiver noise power. In Cognitive Radio setup it is not realistic to assume that each secondary user will have this knowledge. So later we modify DualCUSUM to develop GLRCUSUM algorithms which can work with imprecise estimates of the channel gains and receiver noise power. We show that the SNR wall problem encountered in this scenario by other detectors is not experienced by our algorithm. We also analyze the GLRCUSUM algorithms theoretically. We also apply our algorithms for detecting the presence of the primary in an Orthogonal Frequency Division Multiplexing (OFDM) setup. We first consider the Cyclic Prefix (CP) detector, which is considered to be robust to uncertainties in noise power, and further modify the CPdetector to take care of some of the common impairments like Timing offset, Frequency offset and IQ imbalance. We further modify the CPdetector to work under frequency selective channel. We also consider the energy detector under different impairments and show that the sequential detection based energy detectors outperform cyclic prefix based Detectors.

Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.

One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.

Master’s thesis belongs to the Cognitive radio network sphere. These networks utilize frequency spectrum more effectively than networks used in present radio communications. The Cognitive radio concept makes coexistence of classic and cognitive radio networks possible. Attention is aimed at spectrum sensing as the key task of the Cognitive radio. Main properties of the cyclostationary detector, as the detector, that reaches high probability of the detection at a very low signal to noise ratio with apriori knowledge of the transmitted signal's cyclic frequency, are examined in this paper. The OFDM signals, that inherit cyclostationarity from cyclic prefix, used in the real systems have been chosen for testing the properties of the detector. The influences of decimation and multipath propagation on the probability of detection are quantitatively expressed. The optimal values for the weights of the multicycle detector are determined.

In wireless communication systems, one of the most important resources being focused on all the researchers is spectrum. A cognitive radio (CR) system is one of the efficient ways to access the radio spectrum opportunistically, and efficiently use the available underutilized licensed spectrum. Spectrum utilization can be significantly enhanced by developing more applications with adopting CR technology. CR systems are implemented using a radio technology called software-defined radios (SDR). SDR provides a flexible and cost-effective solution to fulfil the requirements of end users. We can see a lot of innovations in Internet of Things (IoT) and increasing number of smart devices. Hence, a CR system application involving an IoT device is studied in this thesis. Opportunistic spectrum access involves two tasks of CR system: spectrum sensing and dynamic spectrum access. The functioning of the CR system is rest upon the spectrum sensing. There are different spectrum sensing techniques used to detect the spectrum holes and a few of them are discussed here in this thesis. The simplest and easiest to implement energy detection spectrum sensing technique is used here to implement the CR system. Dynamic spectrum access involves different models and strategies to access the spectrum. Amongst the available models, an interweave model is more challenging and is used in this thesis. Interweave model needs effective spectrum sensing before accessing the spectrum opportunistically. The system designed and simulated in this thesis is capable of transmitting an output from an IoT device using USRP and GNU radio through accessing the radio spectrum opportunistically.

Dynamic spectrum access enabled by cognitive radio networks are envisioned to drive the next generation wireless networks that can increase spectrum utility by opportunistically accessing unused spectrum. Due to the policy constraint that there could be no interference to the primary (licensed) users, secondary cognitive radios have to continuously sense for primary transmissions. Typically, sensing reports from multiple cognitive radios are fused as stand-alone observations are prone to errors due to wireless channel characteristics. Such dependence on cooperative spectrum sensing is vulnerable to attacks such as Secondary Spectrum Data Falsification (SSDF) attacks when multiple malicious or selfish radios falsify the spectrum reports. Hence, there is a need to quantify the trustworthiness of radios that share spectrum sensing reports and devise malicious node identification and robust fusion schemes that would lead to correct inference about spectrum usage. In this work, we propose an anomaly monitoring technique that can effectively capture anomalies in the spectrum sensing reports shared by individual cognitive radios during cooperative spectrum sensing in a multi-channel distributed network. Such anomalies are used as evidence to compute the trustworthiness of a radio by its neighbours. The proposed anomaly monitoring technique works for any density of malicious nodes and for any physical environment. We propose an optimistic trust heuristic for a system with a normal risk attitude and show that it can be approximated as a beta distribution. For a more conservative system, we propose a multinomial Dirichlet distribution based conservative trust framework, where Josang*s Belief model is used to resolve any uncertainty in information that might arise during anomaly monitoring. Using a machine learning approach, we identify malicious nodes with a high degree of certainty regardless of their aggressiveness and variations introduced by the pathloss environment. We also propose extensions to the anomaly monitoring technique that facilitate learning about strategies employed by malicious nodes and also utilize the misleading information they provide. We also devise strategies to defend against a collaborative SSDF attack that is launched by a coalition of selfish nodes. Since, defense against such collaborative attacks is difficult with popularly used voting based inference models or node centric isolation techniques, we propose a channel centric Bayesian inference approach that indicates how much the collective decision on a channels occupancy inference can be trusted. Based on the measured observations over time, we estimate the parameters of the hypothesis of anomalous and non-anomalous events using a multinomial Bayesian based inference. We quantitatively define the trustworthiness of a channel inference as the difference between the posterior beliefs associated with anomalous and non-anomalous events. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data. Subsequently, we propose robust fusion models which utilize the trusts of the nodes to improve the accuracy of the cooperative spectrum sensing decisions. In particular, we propose three fusion models: (i) optimistic trust based fusion, (ii) conservative trust based fusion, and (iii) inversion based fusion. The former two approaches exclude untrustworthy sensing reports for fusion, while the last approach utilizes misleading information. All schemes are analyzed under various attack strategies. We propose an asymmetric weighted moving average based trust management scheme that quickly identifies on-off SSDF attacks and prevents quick trust redemption when such nodes revert back to temporal honest behavior. We also provide insights on what attack strategies are more effective from the adversaries* perspective. Through extensive simulation experiments we show that the trust models are effective in identifying malicious nodes with a high degree of certainty under variety of network and radio conditions. We show high true negative detection rates even when multiple malicious nodes launch collaborative attacks which is an improvement over existing voting based exclusion and entropy divergence techniques. We also show that we are able to improve the accuracy of fusion decisions compared to other popular fusion techniques. Trust based fusion schemes show worst case decision error rates of 5% while inversion based fusion show 4% as opposed majority voting schemes that have 18% error rate. We also show that the proposed channel centric Bayesian inference based trust model is able to distinguish between attacked and non-attacked channels for both static and dynamic collaborative attacks. We are also able to show that attacked channels have significantly lower trust values than channels that are not– a metric that can be used by nodes to rank the quality of inference on channels.

With the reality that diverse air interfaces and dissimilar access networks coexist, accompanied by the trend that dynamic spectrum access (DSA) is allowed and will be gradually employed, cognition and cooperation form a promising framework to achieve the ideality of seamless ubiquitous connectivity in future communication networks. In this dissertation, the cognitive gateway (CG), conceived as a special cognitive radio (CR) node, is proposed and designed to facilitate universal interoperability among incompatible waveforms. A proof-of-concept prototype is built and tested. Located in places where various communication nodes and diverse access networks coexist, the CG can be easily set up and works like a network server with differentiated service (Diffserv) architecture to provide automatic traffic relaying and link establishment. The author extracts a scalable '“source-CG-destination“ snapshot from the entire network and investigates the key enabling technologies for such a snapshot. The CG features provide universal interoperability, which is enabled by a generic waveform representation format and the reconfigurable software defined radio platform. According to the trend of an all IP-based solution for future communication systems, the term “waveform“ in this dissertation has been defined as a protocol stack specification suite. The author gives a generic waveform representation format based on the five-layer TCP/IP protocol stack architecture. This format can represent the waveforms used by Ethernet, WiFi, cellular system, P25, cognitive radios etc. A significant advantage of CG over other interoperability solutions lies in its autonomy, which is supported by appropriate signaling processes and automatic waveform identification. The service process in a CG is usually initiated by the users who send requests via their own waveforms. These requests are transmitted during the signaling procedures. The complete operating procedure of a CG is depicted as a waveform-oriented cognition loop, which is primarily executed by the waveform identifier, scenario analyzer, central controller, and waveform converter together. The author details the service process initialized by a primary user (e.g. legacy public safety radio) and that initialized by a secondary user (e.g. CR), and describes the signaling procedures between CG and clients for the accomplishment of CG discovery, user registration and un-registration, link establishment, communication resumption, service termination, route discovery, etc. From the waveforms conveyed during the signaling procedures, the waveform identifier extracts the parameters that can be used for a CG to identify the source waveform and the destination waveform. These parameters are called “waveform indicators.“ The author analyzes the four types of waveforms of interest and outlines the waveform indicators for different types of communication initiators. In particular, a multi-layer waveform identifier is designed for a CG to extract the waveform indicators from the signaling messages. For the physical layer signal recognition, a Universal Classification Synchronization (UCS) system has been invented. UCS is conceived as a self-contained system which can detect, classify, synchronize with a received signal and provide all parameters needed for physical layer demodulation without prior information from the transmitter. Currently, it can accommodate the modulations including AM, FM, FSK, MPSK, QAM and OFDM. The design and implementation details of a UCS have been presented. The designed system has been verified by over-the-air (OTA) experiments and its performance has been evaluated by theoretical analysis and software simulation. UCS can be ported to different platforms and can be applied for various scenarios. An underlying assumption for UCS is that the target signal is transmitted continually. However, it is not the case for a CG since the detection objects of a CG are signaling messages. In order to ensure higher recognition accuracy, signaling efficiency, and lower signaling overhead, the author addresses the key issues for signaling scheme design and their dependence on waveform identification strategy. In a CG, waveform transformation (WT) is the last step of the link establishment process. The resources required for transformation of waveform pairs, together with the application priority, constitute the major factors that determine the link control and scheduling scheme in a CG. The author sorts different WT into five categories and describes the details of implementing the four typical types of WT (including physical layer analog – analog gateway, up to link layer digital – digital gateway, up-to-network-layer digital gateway, and Voice over IP (VoIP) – an up to transport layer gateway) in a practical CG prototype. The issues that include resource management and link scheduling have also been addressed. This dissertation presents a CG prototype implemented on the basis of GNU Radio plus multiple USRPs. In particular, the service process of a CG is modeled as a two-stage tandem queue, where the waveform identifier queues at the first stage can be described as M/D/1/1 models and the waveform converter queue at the second stage can be described as G/M/K/K model. Based on these models, the author derives the theoretical block probability and throughput of a CG. Although the “source-CG-destination” snapshot considers only neighboring nodes which are one-hop away from the CG, it is scalable to form larger networks. CG can work in either ad-hoc or infrastructure mode. Utilizing its capabilities, CG nodes can be placed in different network architectures/topologies to provide auxiliary connectivity. Multi-hop cooperative relaying via CGs will be an interesting research topic deserving further investigation. / Ph. D.

This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-of of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine's capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work. / Ph. D.

Radio spectrum has become increasingly scarce due to the proliferation of new wireless communication services. This problem has been exacerbated by fixed bandwidth licensing policies that often lead to spectral underutilization. Cognitive radio networks (CRN) can address this issue using flexible spectrum management that permits unlicensed (secondary) users to access the licensed spectrum. Supporting real-time quality-of-service (QoS) in CRNs however, is very challenging, due to the random spectrum availability induced by the licensed (primary) user activity. This thesis considers the problem of real-time voice transmission in CRNs with an emphasis on secondary network ``friendliness''. Friendliness is measured by the secondary real-time voice capacity, defined as the number of connections that can be supported, subject to typical QoS constraints. The constant bit rate (CBR) air interface case is first assumed. An offline scheduler that maximizes friendliness is derived using an integer linear program (ILP) that can be solved using a minimum cost flow graph construction. Two online primary scheduling algorithms are then introduced. The first algorithm is based on shaping the primary spectral hole patterns subject to primary QoS constraints. The second applies real-time scheduling to both primary traffic and virtual secondary calls. The online scheduling algorithms are found to perform well compared to the friendliness upper bound. Extensive simulations of the primary friendly schedulers show the achievable secondary voice capacity for a variety of parameters compared to non-friendly primary scheduling. The thesis then considers the variable bit rate (VBR) air interface option for primary transmissions. Offline and online approaches are taken to generate a primary VBR traffic schedule that is friendly to secondary voice calls. The online VBR schedulers are found to perform well compared to the friendliness upper bound. Simulation results are presented that show the effect of the primary traffic load and primary network delay tolerance on the primary network friendliness level towards potential secondary voice traffic. Finally, secondary user friendliness is considered from an infrastructure deployment point of view. A cooperative framework is proposed, which allows the primary traffic to be relayed by helper nodes using decode-and-forward (DF) relaying. This approach decreases the primary traffic channel utilization, which, in turn, increases the capacity available to potential secondary users. A relay selection optimization problem is first formulated that minimizes the primary channel utilization. A greedy algorithm that assigns relay nodes to primary data flows is introduced and found to perform well compared to the optimum bound. Results are presented that show the primary network friendliness for different levels of primary channel utilization. / Dissertation / Doctor of Philosophy (PhD)