Public Safety Cognitive Radio (PSCR) is a Software Defined Radio(SDR) developed by the Center for Wireless Telecommunications (CWT) at Virginia Tech. PSCR can configure itself to interoperate with any public safety waveform it finds during the scan procedure. It also offers users the capability to scan/classify both analog and digital waveforms. The current PSCR architecture can only run on a general purpose processor and hence is not deployable to the public safety personnel. In the first part of this thesis an Android based control application for the PSCR on a Beagle Board(BB) and the GUI for the control application are developed. The Beagle Board is a low-cost, fanless single board computer that unleashes laptop-like performance and expandability. The Android based Nexus One connected to the Beagle Board via USB is used to control the Beagle Board and enable operations like scan, classify, talk, gateway etc. In addition to the features that exist in the current PSCR a new feature that enables interoperation with P25 (CPFSK modulation) protocol based radios is added. In this effort of porting the PSCR to Beagle Board my contributions are the following (i) communication protocol between the Beagle Board and the Nexus One (ii) PSCR control application on the Android based Nexus One (iii) detection/classification of P25 protocol based radios. In the second part of this thesis, a prototype testbed of a Dynamic Spectrum Access (DSA) broker that uses the Beagle Board PSCR based sensor/classifier is developed. DSA in simple terms is a concept that lets the user without license (secondary user) to a particular frequency access that frequency, when the licensed user (primary user) is not using it. In the proposed testbed we have two Beagle Board based sensor/classifiers that cooperatively scan the spectrum and report the results to the central DSA broker. The DSA broker then identifies the frequency spectrum without primary users and informs the secondary users about the free spectrum. The secondary users can then communicate among each other using the frequency band allocated by the DSA broker. When the primary user enters the spectrum occupied by the secondary user, the DSA broker instructs the secondary user to use a different spectrum. Based on the experiments conducted on the testbed setup in the CWT lab environment, the average time taken by the DSA broker to detect the presence of primary user is 0.636 secs and the average time taken for the secondary user to leave the frequency band that interferes with the primary user is 0.653 secs. / Master of Science

The work presented in this thesis concerns one of the key enabling techniques related to cognitive radio functionalities which is spectrum sensing as well as cooperative spectrum sensing. As cooperative spectrum sensing (CSS) approaches are commonly used for combating fading and improving detection performance, their performances using different combining rules have been analyzed. Due to the low implementation complexity, Goodness of Fit based spectrum sensing has been studied for cognitive radio applications. Motivated by its nice features of local sensing, a distributed consensus spectrum sensing for CR, has been presented, integrating a Goodness of Fit based spectrum sensing scheme. / Le travail présenté dans cette thèse concerne l'une des techniques clés dans les fonctionnalités de la radio cognitive qui est la détection du spectre ainsi que la détection coopérative du spectre. La détection coopérative est couramment utilisée pour la lutte contre l’évanouissement du canal à fin d'améliorer les performances de la détection. Les performances de la détection coopérative en utilisant différentes règles de fusion ont été analysées. En raison sa simplicité, la détection du spectre par les testes d’adéquation a été étudiée pour les applications de la radio cognitive. Motivé par la caractéristique d’être indépendant de bruit, ces testes d’adéquation ont été utilisés pour la détection locale, pour la détection coopérative distribuée. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

No / The IEEE 802.22 is the first worldwide standard for wireless regional area network (WRAN) based on cognitive radio techniques. It provides access to use unused TV band without causing any harmful interference to the incumbents. This paper aims to elaborate the significance of the Spectrum Manager (SM) in WRAN Base Station (BS). It is responsible to maintain spectrum availability information of the cell. Using incumbent database, geolocation and spectrum sensing results, the SM defines the status of the channels with respect to incumbent detection. On the basis of channel status, the SM classifies the channel into different categories. A pseudocode has been proposed for the SM to perform channel decision process in two steps. Spectrum etiquette procedure is activated due to incumbent detection, neighboring WRAN cell detection/update, operating channel switching request and contention request obtained from neighboring WRAN cells. An example is given to demonstrate this procedure in a WRAN cells. Spectrum handoff mechanisms is initiated through the SM either when primary user is detected on the licensed channel or when the specified transmission time is terminated as discussed in the IEEE 802.22 standard. Other responsibilities of the SM are to impose IEEE 802.22 policies within the cell to ensure incumbent protection and maintain QoS in WRAN system. The policies are concerned with events and their corresponding actions. The SM also controls the sensing behavior of the Spectrum Sensing Automation (SSA), where SSA is an entity that must be present in all IEEE 802.22 devices which performs spectrum sensing through spectrum sensing function (SSF) after receiving request from SM.

This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel. Finally, a special filter-bank transform which is called slantlet packet transform (SPT) is developed and used to estimate the uncertain noise power. Moreover, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decomposition.

Spectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to una ordably high sampling rates at energy-constrained SUs. Compressive sensing (CS) was developed to overcome this issue, which enables sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low, spectral signals exhibit a natural sparsity in frequency domain, which motivates the promising application of CS in wideband CRNs. This thesis proposes several e ective algorithms for invoking CS in wideband CRNs. Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing computational complexity of signal recovery. Additionally, a low-complexity algorithm is designed, in which original signals are recovered with fewer measurements, as geolocation database is invoked to provide prior information. Moreover, security enhancement issue of CRNs is addressed by proposing a malicious user detection algorithm, in which data corrupted by malicious users are removed during the process of matrix completion (MC). One key spotlight feature of this thesis is that both real-world signals and simulated signals over TVWS are invoked for evaluating network performance. Besides invoking CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by performing signal recovery at a remote fusion center.

Wide-band spectrum sensing is an approach for finding spectrum holes within a wideband signal with less complexity/delay than the conventional approaches. In this thesis, we propose four different algorithms for detecting the holes in a wide-band spectrum and finding the sparsity level of compressive signals. The first algorithm estimates the spectrum in an efficient manner and uses this estimation to find the holes. The second algorithm detects the spectrum holes by reconstructing channel energies instead of reconstructing the spectrum itself. In this method, the signal is fed into a number of filters. The energies of the filter outputs are used as the compressed measurement to reconstruct the signal energy. The third algorithm employs two information theoretic algorithms to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level.

Wide-band spectrum sensing is an approach for finding spectrum holes within a wideband signal with less complexity/delay than the conventional approaches. In this thesis, we propose four different algorithms for detecting the holes in a wide-band spectrum and finding the sparsity level of compressive signals. The first algorithm estimates the spectrum in an efficient manner and uses this estimation to find the holes. The second algorithm detects the spectrum holes by reconstructing channel energies instead of reconstructing the spectrum itself. In this method, the signal is fed into a number of filters. The energies of the filter outputs are used as the compressed measurement to reconstruct the signal energy. The third algorithm employs two information theoretic algorithms to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level.

Spectrum sensing is an essential pre-processing step of cognitive radio technology for dynamic radio spectrum management. One of the main functions of Cognitive radios is to detect the unused spectrum and share it without harmful interference with other users. The detection of signal components present within a determined frequency band is an important requirement of any sensing technique. Most methods are restricted to the detection of the spectral lines. However, these methods may not comply with the needs imposed by practical applications.  This master thesis work presents a novel method to detect significant spectral components in measured non-flat spectra by classifying them in two groups: signal and noise frequency lines. The algorithm based on Fisher’s discriminant analysis, aside from the detection of spectral lines, estimates the magnitude of the spectral lines and provides a measure of the quality of classification to determine if a spectral line was incorrectly classified. Furthermore, the frequency lines with higher probability of misclassification are regrouped and the validation process recomputed, which results in lower probabilities of misclassification. The proposed automatic detection algorithm requires no user interaction since any prior knowledge about the measured signal and the noise power is needed. The presence or absence of a signal regardless of the shape of the spectrum can be detected. Hence, this method becomes a strong basis for high-quality operation mode of cognitive radios. Simulation and measurement results prove the advantages of the presented technique. The performance of the technique is evaluated for different signal-to-noise ratios (SNR) ranging from 0 to -21dB as required by the IEEE standard for smart radios. The method is compared with previous signal detection methods.

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Radio spectrum is becoming scarce resource due to increase in the usage of wireless communication devices. However studies have revealed that most of the allotted spectrum is not used effectively. Given the demand for more bandwidth and the amount of underutilized spectrum, DSA (Dynamic Spectrum Access) networks employing cognitive radios are a solution that can revolutionize the telecommunications industry, significantly changing the way we use spectrum resources, and design wireless systems and services. Cognitive radio has improve the spectral efficiency of licensed radio frequency bands by accessing unused part of the band opportunistically without interfering with a license primary user PU. In this thesis we investigate the effects on the quality of service (QoS) performance of spectrum management techniques for the connection-based channel usage behavior for Secondary user (SU). This study also consider other factors such as spectrum sensing time, spectrum handoff and generally distributed service time and channel contention for different SUs. The preemptive resume priority M/G/1 queuing theory is used to characterize the above mentioned effects. The proposed structure of the model can integrate various system parameters such spectrum sensing, spectrum decision, spectrum sharing and spectrum handoff. / Sajid Mahmood 0046-762788990 Mujeeb Abdullah 0046-760908069