As the demand for wireless communication services grows quickly, spectrum scarcity has been on the rise sharply. In this context, cognitive radio (CR) is being viewed as a new intelligent technology to solve the deficiency of fixed spectrum assignment policy in wireless communications. Spectrum sensing is one of the most fundamental technologies to realise dynamic spectrum access in cognitive radio networks. It requires high accuracy as well as low complexity. In this thesis, a novel adaptive threshold setting algorithm is proposed to optimise the trade-off between detection and false alarm probability in spectrum sensing while satisfying sensing targets set by the IEEE 802.22 standard. The adaptive threshold setting algorithm is further applied to minimise the error decision probability with varying primary users' spectrum utilisations. A closed-form expression for the error decision probability, satisfied SNR value, number of samples and primary users' spectrum utilisation ratio are derived in both fixed and the proposed adaptive threshold setting algorithms. By implementing both Welch and wavelet based energy detectors, the adaptive threshold setting algorithm demonstrates a more reliable and robust sensing result for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. Furthermore, the wavelet de-noising method is applied to improve the sensing performance when there is insu cient number of samples. Finally, a novel database assisted spectrum sensing algorithm is proposed for a secondary access of the TV White Space (TVWS) spectrum. The proposed database assisted sensing algorithm is based on the developed database assisted approach for detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making and Special Events (PMSE), but assisted by spectrum sensing to further improve the protection to primary users. Monte-Carlo simulations show a higher SUs' spectrum efficiency can be obtained for the proposed database assisted sensing algorithm than the existing stand-alone database assisted or sensing models.

Cognitive radios (CRs) have been proposed as a promising solution for improving spectrum utilization via opportunistic spectrum sharing. In a CR network environment, primary (licensed) users have priority over secondary (unlicensed) users when accessing the wireless channel. Thus, if a malicious secondary user exploits this spectrum access etiquette by mimicking the spectral characteristics of a primary user, it can gain priority access to a wireless channel over other secondary users. This scenario is referred to in the literature as primary user emulation (PUE). This dissertation first covers three approaches for detecting primary user emulation attacks in cognitive radio networks, which can be classified in two categories. The first category is based on cyclostationary features, which employs a cyclostationary calculation to represent the modulation features of the user signals. The calculation results are then fed into an artificial neural network for classification. The second category is based on video processing method of action recognition in frequency domain, which includes two approaches. Both of them analyze the FFT sequences of wireless transmissions operating across a cognitive radio network environment, as well as classify their actions in the frequency domain. The first approach employs a covariance descriptor of motion-related features in the frequency domain, which is then fed into an artificial neural network for classification. The second approach is built upon the first approach, but employs a relational database system to record the motion-related feature vectors of primary users on this frequency band. When a certain transmission does not have a match record in the database, a covariance descriptor will be calculated and fed into an artificial neural network for classification. This dissertation is completed by a novel PUE detection approach which employs a distributed sensor network, where each sensor node works as an independent PUE detector. The emphasis of this work is how these nodes collaborate to obtain the final detection results for the whole network. All these proposed approaches have been validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform.

<p>The communication systems are changing. Cognitive Radio is an automatic adaptative system to improve the spectrum efficiency. It has intelligence to adapt itself to the environment to improve the transmission performancies. For this system, spectrum sensing function is very important so a signal detector is necessary. In this work a signal detector has been implemented in GNU Radio environment. GNU Radio is a platform that respects the Cognitive Radio aproach. It is flexible, software defined and cheap.</p>

The communication systems are changing. Cognitive Radio is an automatic adaptative system to improve the spectrum efficiency. It has intelligence to adapt itself to the environment to improve the transmission performancies. For this system, spectrum sensing function is very important so a signal detector is necessary. In this work a signal detector has been implemented in GNU Radio environment. GNU Radio is a platform that respects the Cognitive Radio aproach. It is flexible, software defined and cheap.

OFDM is a fast growing technology in the area of wireless communication due to its numerous advantages and applications. The current and future technologies in the area of wireless communications like WiMAX, WiFi, LTE, MBWA and DVB-T uses the OFDM signals. The OFDM technology is applicable to the radio communication as well as the acoustic communication. Though the licensed spectrum is intended to be used only by the spectrum owners, Cognitive radio is a concept of reusing this licensed spectrum in an unlicensed manner. Cognitive radio is motivated by the measurements of spectrum utilization . Cognitive radio must be able to detect very weak primary users signal and to keep the interference level at a maximum acceptable level. Hence spectrum sensing is an essential part of the cognitive radio. Spectrum is a scarce resource and spectrum sensing is the process of identifying the unused spectrum, without causing any harm to the existing primary user’s signal. The unused spectrum is referred to as spectrum hole or white space and this spectrum hole could be reused by the cognitive radio. This thesis work focuses on implementing primary acoustic transmitter to transmit the OFDM signals from a computer through loudspeaker and receive the signals through a microphone. Then by applying different detection methods on the received OFDM signal for detection of the spectrum hole, the performance of these detection methods is compared here. The commonly used detection methods are power spectrum estimation, energy detection and second–order statistics (GLRT approach, Autocorrelation Function (ACF) detection and cyclostationary feature detection ). The detector based on GLRT approach exploits the structure of the OFDM signal by using the second order statistics of the received data. The thesis mainly focuses on GLRT approach and ACF detectors and compare their performance.

Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users. In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback.

Thesis (M.S.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 93).

Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users. In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback.

Cognitive radio (CR) is a promising technique to improve spectrum efficiency for wireless communications. This thesis focuses on the resource allocation in two kinds of CR networks (CRNs), traditional CRNs (TCRNs) and cooperative CRNs (CCRNs). In TCRNs, CR sources and destinations communicate directly. By exploring the heterogeneity among CRs, a prioritized CSMA/CA is proposed for demand-matching spectrum allocation. A distributed game is formulated and no-regret learning is adopted to solve the game. Simulation results indicate increase on the number of satisfied CRs. In CCRNs, some nodes are selected as relays to assist the communication. A two-layer auction game is proposed with the first layer performing spectrum allocation and relay formation, and the second layer performing relay allocation. These two layers interact and jointly solve the resource allocation problem. Simulation results show that, compared to counterparts, both the network throughput and convergence time can be improved.

Cognitive radio (CR) is a promising technique to improve spectrum efficiency for wireless communications. This thesis focuses on the resource allocation in two kinds of CR networks (CRNs), traditional CRNs (TCRNs) and cooperative CRNs (CCRNs). In TCRNs, CR sources and destinations communicate directly. By exploring the heterogeneity among CRs, a prioritized CSMA/CA is proposed for demand-matching spectrum allocation. A distributed game is formulated and no-regret learning is adopted to solve the game. Simulation results indicate increase on the number of satisfied CRs. In CCRNs, some nodes are selected as relays to assist the communication. A two-layer auction game is proposed with the first layer performing spectrum allocation and relay formation, and the second layer performing relay allocation. These two layers interact and jointly solve the resource allocation problem. Simulation results show that, compared to counterparts, both the network throughput and convergence time can be improved.