Spectrum sensing plays a key role for radio resource awareness in cognitive radio. To enhance the capabilities of cognitive radio nodes, exploiting the spatial resource in addition to frequency and time re-sources seems reasonable. This thesis investigates the possibility of exploiting the spatial resources during sensing and transmission using sector antennas which is also termed as directional spectrum sensing and transmission. The measured radiation patterns from fabricated antenna and radiation patterns obtained from analytical expressions representing circular array of dipole are used for performance analysis. A ray tracer tool is used for modelling the urban environment as well as for wave propagation simulation. The power angular profiles obtained at different locations are further processed in MATLAB using measured and analytical radiation patterns to evaluate the performance in terms of spatial opportunity and detection of weak primary signals. The results show that exploiting the spatial dimension in spectrum sensing using sector antennas increase the opportunities for secondary communication and also improves the detection of primary signals as compared with an omni-directional antenna. Additionally, directional sensing and trans-mission are studied together using analytical radiation patterns. The results show that the service probability as well as range of communica-tion increases with an increase in number of sectors but saturation is achieved when nine sectors are used, indicating that six sectors antenna is the optimum choice for exploring the spatial resource in cognitive radio in a typical multipath urban environment.

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.

Cognitive radio (CR) paradigm is a new radio technology proposed to solve spectrum scarcity and underutilization. Central to CR is spectrum sensing (SS), which is responsible for detecting unoccupied frequencies. Since Detection techniques differ in their performance, selecting the optimal detection method to locally perform SS has received significant attention. This research work aims to enhance the reliability of local detection decisions, under low SNR, by developing a spectrum sensing that can take advantage of multiple detection techniques. This model can either select the optimal technique or make these techniques cooperate with one another to achieve better sensing performance. The model performance is measured with respect to detection and false alarm probability as well as sensing time. To develop this model, the performance of three detection techniques is evaluated and compared. Furthermore, the voting and the maximum a posteriori probability (MAP) fusion models were developed and employed to combine spectrum sensing results obtained from the three techniques. It is concluded that the cyclostationary feature detection technique is a superior detector in low SNR situations. MAP fusion model is found to be more reliable than the voting model.

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Recently, interest has been shown in cognitive radio (CR) systems since they can op- portunistically access unused spectrum bands thereby increasing usable communication capacity. Spectrum sensing has been identified as a key function to ensure that CR can detect spectrum holes. In a CR network, a fast and accurate spectrum sensing scheme is important. Spectrum sensing can be viewed as a signal detection problem. Most of the existing spectrum sensing schemes are based on fixed sample size detectors which means that their sensing time is preset and fixed. However, the work of Wald [27] showed that a detector based on sequential detection requires less average sensing time than a fixed sample size detector. In this thesis, we have applied the method of sequential detection to reduce the average sensing time. Simulation results have shown that, compared to the fixed sample size energy detector, a sequential detector can reduce sensing time by up to 85% in the AWGN channel for the same detection performance. In order to limit sensing time, especially in a fading environment, a truncated sequential detector is developed. The simulation results show that the truncated sequential detector requires less sensing time than the sequential detector, but the performance degrades due to truncation. Finally, a cooperative spectrum sensing scheme is used where each individual sensor uses a sequential detector. The combining rule used at the fusion center is a selection combining rule. Simulation results show that the proposed cooperative spectrum sensing scheme can reduce the sensing time compared to the individual spectrum sensing scheme.

Cognitive radio (CR) paradigm is a new radio technology proposed to solve spectrum scarcity and underutilization. Central to CR is spectrum sensing (SS), which is responsible for detecting unoccupied frequencies. Since Detection techniques differ in their performance, selecting the optimal detection method to locally perform SS has received significant attention. This research work aims to enhance the reliability of local detection decisions, under low SNR, by developing a spectrum sensing that can take advantage of multiple detection techniques. This model can either select the optimal technique or make these techniques cooperate with one another to achieve better sensing performance. The model performance is measured with respect to detection and false alarm probability as well as sensing time. To develop this model, the performance of three detection techniques is evaluated and compared. Furthermore, the voting and the maximum a posteriori probability (MAP) fusion models were developed and employed to combine spectrum sensing results obtained from the three techniques. It is concluded that the cyclostationary feature detection technique is a superior detector in low SNR situations. MAP fusion model is found to be more reliable than the voting model.

Cognitive radio is a promising technology that improves the spectral utilisation by allowing unlicensed secondary users to access underutilised frequency bands in an opportunistic manner. This task can be carried out through spectrum sensing: the secondary user monitors the presence of primary users over the radio spectrum periodically to avoid harmful interference to the licensed service. Traditional energy based sensing methods assume the value of noise power as prior knowledge. They suffer from the noise uncertainty problem as even a mild noise level mismatch will lead to significant performance loss. Hence, developing an efficient robust detection method is important. In this thesis, a novel sensing technique using the F-test is proposed. By assuming a multiple antenna assisted receiver, this detector uses the F-statistic as the test statistic which offers absolute robustness against the noise variance uncertainty. In addition, since the channel state information (CSI) is required to be known, the impact of CSI uncertainty is also discussed. Results show the F-test based sensing method performs better than the energy detector and has a constant false alarm probability, independent of the accuracy of the CSI estimate. Another main topic of this thesis is to address the sensing problem for non-Gaussian noise. Most of the current sensing techniques consider Gaussian noise as implied by the central limit theorem (CLT) and it offers mathematical tractability. However, it sometimes fails to model the noise in practical wireless communication systems, which often shows a non-Gaussian heavy-tailed behaviour. In this thesis, several sensing algorithms are proposed for non-Gaussian noise. Firstly, a non-parametric eigenvalue based detector is developed by exploiting the eigenstructure of the sample covariance matrix. This detector is blind as no information about the noise, signal and channel is required. In addition, the conventional energy detector and the aforementioned F-test based detector are generalised to non-Gaussian noise, which require the noise power and CSI to be known, respectively. A major concern of these detection methods is to control the false alarm probability. Although the test statistics are easy to evaluate, the corresponding null distributions are difficult to obtain as they depend on the noise type which may be unknown and non-Gaussian. In this thesis, we apply the powerful bootstrap technique to overcome this difficulty. The key idea is to reuse the data through resampling instead of repeating the experiment a large number of times. By using the nonparametric bootstrap approach to estimate the null distribution of the test statistic, the assumptions on the data model are minimised and no large sample assumption is invoked. In addition, for the F-statistic based method, we also propose a degrees-of-freedom modification approach for null distribution approximation. This method assumes a known noise kurtosis and yields closed form solutions. Simulation results show that in non-Gaussian noise, all the three detectors maintain the desired false alarm probability by using the proposed algorithms. The F-statistic based detector performs the best, e.g., to obtain a 90% detection probability in Laplacian noise, it provides a 2.5 dB and 4 dB signal-to-noise ratio (SNR) gain compared with the eigenvalue based detector and the energy based detector, respectively.

Abstract Spread spectrum communication systems may be affected by other types of signals called outliers. These coexisting signals are typically narrow (or concentrated) in the considered domain. This thesis considers two areas of outlier detection, namely the concentrated interference suppression (IS) and concentrated signal detection. The focus is on concentrated signal extraction using blind, iterative and low-complex consecutive mean excision (CME) -based algorithms that can be applied to both IS and detection. A summary of results obtained from studying the performance of the existing IS methods, namely the CME, the forward CME (FCME) and the transform selective IS algorithms (TSISA), is presented. Accurate threshold parameter values for the FCME algorithm are defined. These accurate values are able to control the false alarm rate. The signal detection capability of the CME algorithms is studied and analyzed. It is noticed that the CME algorithms are able to detect signals, but they are not able to estimate signal parameters such as the bandwidth. The presented generic shape-based analysis leads to the limits of detection in which the concentrated signals can be detected. These limits enable checking fast whether the signal is detectable or not without time consuming computer simulations. The performance of the TSISA method is evaluated. Simulation results demonstrate that the TSISA method is able to suppress several types of concentrated interfering signals with a reasonable computational complexity. Finally, new CME-based methods are proposed and evaluated. The proposed methods are the extended TSISA method for IS and the localization algorithm based on double-thresholding (LAD), LAD with normalized thresholds (LAD NT), LAD with adjacent cluster combining (LAD ACC) and two-dimensional (2-D) LAD methods for detection. The simulations indicate that the extended TSISA method has a good performance against several types of concentrated interfering signals. The narrowband signal detection capability of the LAD methods is studied. Numerical results show that the proposed LAD methods are able to detect and localize signals in their domain, and they are able to estimate the number of narrowband signals and their parameters, including, for example, bandwidths and signal-to-noise ratio (SNR) values. The simulations show that the LAD methods outperform the CME algorithms, and ACC and 2-D LAD methods outperform the original LAD method. The LAD methods are also proposed to be used for spectrum sensing purposes in cognitive radios.

The covariance estimation is one of the most critical tasks in multivariate statistical analysis. In many applications, reliable estimation of the covariance matrix, or scatter matrix in general, is required. The performance of the classical maximum likelihood method relies a great deal on the validity of the model assumption. Since the assumptions are often approximately correct, many robust statistical methods have been proposed to be robust against the deviation from the model assumptions. M-estimator is an important class of robust estimator of the scatter matrix. The properties of these robust estimators under high dimensional setting, which means the number of dimensions has the same order of magnitude as the number of observations, is desirable. To study these, random matrix theory is a very important tool. With high dimensional properties of robust estimators, we introduced a new method for blind spectrum sensing in cognitive radio networks.

Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this new learning algorithm for prediction of channel states outperforms the traditional BP algorithm and Hybrid GA-PSO method with respect to classification accuracy, probability of misdetection, and Probability of false alarm.