Faced with imminent spectrum scarcity largely due to inflexible licensed band arrangements, cognitive radio (CR) has been proposed to facilitate higher spectrum utilization by allowing cognitive users (CUs) to access the licensed bands without causing harmful interference to primary users (PUs). To achieve this without the aid of PUs, the CUs have to perform spectrum sensing reliably detecting the presence or absence of PU signals. Without reliable spectrum sensing, the discovery of spectrum opportunities will be inefficient, resulting in limited utilization enhancement. This dissertation examines three major techniques for spectrum sensing, which are matched filter, energy detection, and cyclostationary feature detection. After evaluating the advantages and disadvantages of these techniques, we narrow down our research to a focus on cyclostationary feature detection (CFD). Our first contribution is to boost performance of an existing and prevailing CFD method. This boost is achieved by our proposed optimal and sub-optimal schemes for identifying best hypothesis test points. The optimal scheme incorporates prior knowledge of the PU signals into test point selection, while the sub-optimal scheme circumvents the need for this knowledge. The results show that our proposed can significantly outperform other existing schemes. Secondly, in view of multi-antenna deployment in CR networks, we generalize the CFD method to include the multi-antenna case. This requires effort to justify the joint asymptotic normality of vector-valued statistics and show the consistency of covariance estimates. Meanwhile, to effectively integrate the received multi-antenna signals, a novel cyclostationary feature based channel estimation is devised to obtain channel side information. The simulation results demonstrate that the errors of channel estimates can diminish sharply by increasing the sample size or the average signal-to-noise ratio. In addition, no research has been found that analytically assessed CFD performance over fading channels. We make a contribution to such analysis by providing tight bounds on the average detection probability over Nakagami fading channels and tight approximations of diversity reception performance subject to independent and identically distributed Rayleigh fading. For successful coexistence with the primary system, interference management in cognitive radio networks plays a prominent part. Normally certain average or peak transmission power constraints have to be placed on the CR system. Depending on available channel side information and fading types (fast or slow fading) experienced by the PU receiver, we derive the corresponding constraints that should be imposed. These constraints indicate that the second moment of interference channel gain is an important parameter for CUs allocating transmission power. Hence, we develop a cooperative estimation procedure which provides robust estimate of this parameter based on geolocation information. With less aid from the primary system, the success of this procedure relies on statistically correlated channel measurements from cooperative CUs. The robustness of our proposed procedure to the uncertainty of geolocation information is analytically presented. Simulation results show that this procedure can lead to better mean-square error performance than other existing estimates, and the effects of using inaccurate geolocation information diminish steadily with the increasing number of cooperative cognitive users.

The aim of this thesis is a software modelling and hardware implementation of spectral sensing algorithm based on cyclostationary based detection. A proposed detector is fully implemented in FPGA and further tested under measurements in real RF environment.

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In this dissertation novel methods are proposed for communicating in interference limited environments as well as detecting such interference. The methods include introducing redundancies into multicarrier signals to make them more robust, applying a novel filtering structure for mitigating radar interference to orthogonal frequency division multiplexing (OFDM) signals and for exploiting the cyclostationary nature of signals to whiten the spectrum in blind signal detection. Data symbols are repeated in both time and frequency across orthogonal frequency division multiplexing (OFDM) symbols, creating a cyclostationary nature in the signal. A Frequency Shift (FRESH) filter can then be applied to the cyclostationary signal, which is the optimal filter and is able to reject interference much better than a time-invariant filter such as the Wiener filter. A novel time-varying FRESH filter (TV-FRESH) filter is developed and its Minimum Mean Squared Error (MMSE) filter weights are found. The repetition of data symbols and their optimal combining with the TV-FRESH filter creates an effect of improving the Bit Error Rate (BER) at the receiver, similar to an error correcting code. The important distinction for the paramorphic method is that it is designed to operate within cyclostationary interference, and simulation results show that the symbol repetition can outperform other error correcting codes. Simulated annealing is used to optimize the signaling parameters, and results show that a balance between the symbol repetition and error correcting codes produces a better BER for the same spectral efficiency than what either method could have achieved alone. The TV-FRESH filter is applied to a pulsed chirp radar signal, demonstrating a new tool to use in radar and OFDM co-existence. The TV-FRESH filter applies a set of filter weights in a periodically time-varying fashion. The traditional FRESH filter is periodically time-varying due to the periodicities of the frequency shifters, but applies time-invariant filters after optimally combine any spectral redundancies in the signal. The time segmentation of the TV-FRESH filter allows spectral redundancies of the radar signal to be exploited across time due to its deterministic nature. The TV-FRESH filter improves the rejection of the radar signal as compared to the traditional FRESH filter under the simulation scenarios, improving the SINR and BER at the output of the filter. The improvement in performance comes at the cost of additional filtering complexity. A time-varying whitening filter is applied to blindly detect interference which overlaps with the desired signal in frequency. Where a time-invariant whitening filter shapes the output spectrum based on the power levels, the proposed time-varying whitener whitens the output spectrum based on the spectral redundancy in the desired signal. This allows signals which do not share the same cyclostationary properties to pass through the filter, improving the sensitivity of the algorithm and producing higher detection rates for the same probability of false alarm as compared to the time-invariant whitener. / Ph. D. / This dissertation proposes novel methods for building robust wireless communication links which can be used to improve their reliability and resilience while under interference. Wireless interference comes from many sources, including other wireless transmitters in the area or devices which emit electromagnetic waves such as microwaves. Interference reduces the quality of a wireless link and depending on the type and severity may make it impossible to reliably receive information. The contributions are both for communicating under interference and being able to detect interference. A novel method for increasing the redundancy in a wireless link is proposed which improves the resiliency of a wireless link. By transmitting additional copies of the desired information the wireless receiver is able to better estimate the original transmitted signal. The digital receiver structure is proposed to optimally combine the redundant information, and simulation results are used to show its improvement over other analogous methods. The second contribution applies a novel digital filter for mitigating interference from a radar signal to an Orthogonal Frequency Division Multiplexing (OFDM) signal, similar to the one which is being used in Long Term Evolution (LTE) mobile phones. Simulation results show that the proposed method out performs other digital filters at the most of additional complexity. The third contribution applies a digital filter and trains it such that the output of the filter can be used to detect the presence of interference. An algorithm which detects interference can tip off an appropriate response, and as such is important to reliable wireless communications. Simulation results are used to show that the proposed method produces a higher probability of detection while reducing the false alarm rate as compared to a similar digital filter trained to produce the same effect.

One component of wireless communications of increasing necessity in both civilian and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that determines the most likely modulation using Grey Relational Analysis with the extraction and combination of multiple signal features. An evaluation of data preprocessing methods is conducted and performance of the classifier is investigated with the addition of each new signal feature used for classification. / Master of Science

Spectrum sensing is an important aspect of cognitive radio systems. In order to efficiently utilize the spectrum, the role of spectrum sensing is essential in cognitive radio networks. The transmitter detection based techniques: energy detection, cyclostationary feature detection, and matched filter detection, is most commonly used for the spectrum sensing. The Energy detection technique is implemented in the 2-hop cooperative cognitive radio network in which Orthogonal Space Time Block Coding (OSTBC) is applied with the Decode and Forward (DF) protocol at the cognitive relays. The Energy detection technique is simplest and gives good results at the higher Signal to Noise Ratio (SNR) values. However, at the low SNR values its performance degrades. Moreover, each transmitter detection technique has a SNR threshold, below which it fails to work robustly. This thesis aims to find the most reliable and accurate spectrum sensing technique in the 2-hop cooperative cognitive radio network. Using Matlab simulations, a comparative analysis of three transmitter detection techniques has been made in terms of higher probability of detection. In order to remove the shortcomings faced by all the three techniques, the Fuzzy-combined logic sensing approach is also implemented and compared with transmitter detection techniques. / Atti Ur Rehman (atti.rehmman@gmail.com) ph: +358-440458080

In a large number of applications arising in various fields of study, time series are approximated using linear MMSE estimates. Such approximations include finite order moving average and autoregressive approximations as well as the causal Wiener filter. In this dissertation, we study two topics related to the estimation of wide sense stationary (WSS) time series using linear MMSE estimates. In the first part of this dissertation, we study the asymptotic behaviour of autoregressive (AR) and moving average (MA) approximations. Our objective is to investigate how faithfully such approximations replicate the original sequence, as the model order as well as the number of samples approach infinity. We consider two aspects: convergence of spectral density of MA and AR approximations when the covariances are known and when they are estimated. Under certain mild conditions on the spectral density and the covariance sequence, it is shown that the spectral densities of both approximations converge in L2 as the order of approximation increases. It is also shown that the spectral density of AR approximations converges at the origin under the same conditions. Under additional regularity assumptions, we show that similar results hold for approximations from empirical covariance estimates. In the second part of this dissertation, we address the problem of detecting interdependence relations within a group of time series. Ideally, in order to infer the complete interdependence structure of a complex system, dynamic behaviour of all the processes involved should be considered simultaneously. However, for large systems, use of such a method may be infeasible and computationally intensive, and pairwise estimation techniques may be used to obtain sub-optimal results. Here, we investigate the problem of determining Granger-causality in an interdependent group of jointly WSS time series by using pairwise causal Wiener filters. Analytical results are presented, along with simulations that compare the performance of a method based on finite impulse response Wiener filters to another using directed information, a tool widely used in literature. The problem is studied in the context of cyclostationary processes as well. Finally, a new technique is proposed that allows the determination of causal connections under certain sparsity conditions.

Cognitive radio is one of the modern techniques for wireless communication systems to utilize the unused spread spectrum effectively. This novel paradigm makes wireless communication possible with less interference. In our work we have investigated one of the functions of cogni- tive radio called spectrum sensing. We have specifically used the method of Cyclostationary fea- ture detection. Spectrum sensing is also a very effective method to detect spectrum holes and to utilize them. We have implemented spectrum sensing technique in these experiments. A signal is randomly generated which could be Binary shift phase keying (BPSK) or Quadrature phase shift keying (QPSK), then this modulated signal is passed through Additive white Gaussian noise (AWGN), The output signal of AWGN is then passed to cyclostationary detector, various func- tions are pre-implemented inside cyclostationary detector and this includes Fast Fourier trans- form (FFT), Auto correlation, Sliding window to identify that signal. Finally we have demon- strated the results of signal to noise ratio to show the performance evaluation of our experiments. The results have shown a decreasing trend in the probability of incorrect detection by increasing signal to noise ratio.

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.

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