M. Tech. Electrical Engineering. / Proposes certain approaches aimed at improving handover from the users point of view, the benefits may include enhanced quality of service, avoidance of handover disruption and reduction of handover rate, irrespective of high traffic loads at the BSs.The proposed scheme improves the network performance by minimising the handover delay time and also enhancing the effective use of radio resources

M. Tech. Electrical Engineering. / Presents an IPv6-based mobility support scheme for vehicular scenarios. The focus of this study is on modelling of an efficient network layer handover scheme suitable for vehicular network environments. In order to achieve this, a Vehicular Fast handovers for Mobile IPv6 (VFMIPv6) network layer protocol has been proposed.

The popularity of ubiquitous Internet services has spurred the fast growth of wireless communications by launching data hungry multimedia applications to mobile devices. Powered by spectrum agile cognitive radios, the newly emerged cognitive radio networks (CRN) are proposed to provision the efficient spectrum reuse to improve spectrum utilization. Unlicensed users in CRN, or secondary users (SUs), access the temporarily idle channels in a secondary and opportunistic fashion while preventing harmful interference to licensed primary users (PUs). To effectively detect and exploit the spectrum access opportunities released from a wide spectrum, the heterogeneous wireless channel characteristics and the underlying prioritized spectrum reuse features need to be considered in the protocol design and resource management schemes in CRN, which plays a critical role in unlicensed spectrum sharing among multiple users. The purpose of this dissertation is to address the challenges of utilizing heterogeneous wireless channels in CRN by its intrinsic dynamic and diverse natures, and build the efficient, scalable and, more importantly, practical dynamic spectrum access mechanisms to enable the cost-effective transmissions for unlicensed users. Note that the spectrum access opportunities exhibit the diversity in the time/frequency/space domain, secondary transmission schemes typically follow three design principles including 1) utilizing local free channels within short transmission range, 2) cooperative and opportunistic transmissions, and 3) effectively coordinating transmissions in varying bandwidth. The entire research work in this dissertation casts a systematic view to address these principles in the design of the routing protocols, medium access control (MAC) protocols and radio resource management schemes in CRN. Specifically, as spectrum access opportunities usually have small spatial footprints, SUs only communicate with the nearby nodes in a small area. Thus, multi-hop transmissions in CRN are considered in this dissertation to enable the connections between any unlicensed users in the network. CRN typically consist of intermittent links of varying bandwidth so that the decision of routing is closely related with the spectrum sensing and sharing operations in the lower layers. An efficient opportunistic cognitive routing (OCR) scheme is proposed in which the forwarding decision at each hop is made by jointly considering physical characteristics of spectrum bands and diverse activities of PUs in each single band. Such discussion on spectrum aware routing continues coupled with the sensing selection and contention among multiple relay candidates in a multi-channel multi-hop scenario. An SU selects the next hop relay and the working channel based upon location information and channel usage statistics with instant link quality feedbacks. By evaluating the performance of the routing protocol and the joint channel and route selection algorithm with extensive simulations, we determine the optimal channel and relay combination with reduced searching complexity and improved spectrum utilization. Besides, we investigate the medium access control (MAC) protocol design in support of multimedia applications in CRN. To satisfy the quality of service (QoS) requirements of heterogeneous applications for SUs, such as voice, video, and data, channels are selected to probe for appropriate spectrum opportunities based on the characteristics and QoS demands of the traffic along with the statistics of channel usage patterns. We propose a QoS-aware MAC protocol for multi-channel single hop scenario where each single SU distributedly determines a set of channels for sensing and data transmission to satisfy QoS requirements. By analytical model and simulations, we determine the service differentiation parameters to provision multiple levels of QoS. We further extend our discussion of dynamic resource management to a more practical deployment case. We apply the experiences and skills learnt from cognitive radio study to cellular communications. In heterogeneous cellular networks, small cells are deployed in macrocells to enhance link quality, extend network coverage and offload traffic. As different cells focus on their own operation utilities, the optimization of the total system performance can be analogue to the game between PUs and SUs in CRN. However, there are unique challenges and operation features in such case. We first present challenging issues including interference management, network coordination, and interworking between cells in a tiered cellular infrastructure. We then propose an adaptive resource management framework to improve spectrum utilization and mitigate the co-channel interference between macrocells and small cells. A game-theory-based approach is introduced to handle power control issues under constrained control bandwidth and limited end user capability. The inter-cell interference is mitigated based upon orthogonal transmissions and strict protection for macrocell users. The research results in the dissertation can provide insightful lights on flexible network deployment and dynamic spectrum access for prioritized spectrum reuse in modern wireless systems. The protocols and algorithms developed in each topic, respectively, have shown practical and efficient solutions to build and optimize CRN.

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.