A fixed spectrum assignment scheme has a problem with resource deficiency in a wireless network. In 2002, the US Federal Communication Commission (FCC) reported that the radio spectrum was 20% to 85% under-utilized. The insufficient use of the spectrum is a critical issue for radio communication; as communication grows, a fixed spectrum becomes more limiting. The FCC then changed its spectrum management policy to make it more flexible by investigating the cognitive radio (CR) approach. Cognitive radio is a type of intelligent radio that explores the radio frequency environment, learns, and decides to use the unused portion of the frequency. The main functions of a CR are sensing, decision making, and sharing. However, these radios have to respect the standard wireless infrastructures by ensuring the least impact with their devices, also known as primary radios. Coexistence between CR systems and primary systems requires dedicated observation processes and interference management. In this thesis, observation from a CR point of view is presented. The overlapping area between a CR transmitter and primary radio (PR) transmitter is analysed so that it can be taken into account. The impact of this area is learnt by simulation and presented in Chapter 4. As a consequence, potential interference is envisaged. Along with observation, we investigate a proper mechanism to better prevent perturbation on PR devices using the Grey model and Kalman filter as a prediction model for predicting the density of primary receivers. In addition, we provide a strategy to combine the obtained observations into a metric that can be used in routing design in the context of coexistence between Cognitive Radio Networks (CRNs) and primary networks. The proposed strategy, using fuzzy logic, is presented in Chapter 5. In this chapter, we investigate how the routing layer reacts and makes the right decisions to maximise the spectrum resources, while avoiding interference with the primary receivers. For instance, a CR node can operate in an overlap region if primary receivers are inactive within this area. Also, we propose a routing mechanism based on the DYMO routing protocol that takes into account the observed relative impact. In the same chapter, we provide some practical scenarios illustrating the usefulness of our proposal. Interconnecting the CR nodes in CRNs is also a critical problem for the establishment of the network. We therefore present a beacon-based dissemination process in Chapter 6. In this chapter, we also describe a practical device designed for cognitive radio experiments. Even though our work affects different protocol layers, the designed framework is cross-layered. Indeed, the different components of the proposed framework access the various layers to retrieve information, process it, and react accordingly. Thus, our work constitutes a cross-layer framework for a local cognitive radio that aims to minimise the interference and maximise the network resources in cognitive radio networks.

/pdf/cross-layer-framework-for-interference-avoidance-in-2keutjz92h.pdf

Cross-layer framework for interference avoidance in cognitive radio ad-hoc networks

Cognitive radio is introduced into the demand response management (DRM) of smart grid with the hope of alleviating the shortage of spectrum resources and improving communication quality. In this pa...

/pdf/demand-response-management-research-based-on-cognitive-radio-3dhsnxsx0l.pdf

Demand Response Management Research Based on Cognitive Radio for Smart Grid

In this paper, we investigate the features of energy detection and cyclostationary feature detection for spectrum sensing. In order to combine their advantages, we propose an adaptive two-stage sensing scheme which performs spectrum sensing using an energy detector first in cognitive radio networks. Then in the second stage, this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, the goal of our proposed scheme is to optimize the probability of detection and sensing speed at the same time. In order to obtain the optimal detection thresholds, we can formulate the detection model as a nonlinear optimization problem. Furthermore, the simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.

Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems

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Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems