Fractal is a recursively-generated object describing a family of complex shapes that possess an inherent self-similarity in their geometrical structure. When used in antenna engineering, fractal geometries provide multi-band characteristics and lowering resonance frequencies by enhancing the space filling property. Moreover, utilizing fractal arrays, controlling side lobe-levels and radiation patterns can be realized. In this thesis, the performance of Koch curve as antenna is investigated. Since fractals are complex shapes, there is no well&ndash / established for mathematical formulation to obtain the radiation properties and frequency response of Koch Curve antennas directly. The Koch curve antennas became famous since they exhibit better frequency response than their Euclidean counterparts. The effect of the parameters of Koch geometry to antenna performance is studied in this thesis. Moreover, modified Koch geometries are generated to obtain the relation between fractal properties and antenna radiation and frequency characteristics.

In this thesis, we analyze allocation of two separate resources in wireless networks: transmit power and buffer space. Controlled allocation of power can provide good performance for both users and the network. Although centralized mechanisms are possible, distributed power control algorithms are preferable for efficient operation of the network. Viewing distributed power allocation as the collection of rational decisions of each user, we make game theoretic problem formulations, devise distributed algorithms and analyze them. First, equilibrium analysis of a vector power control game based on network energy efficiency in a multiple access point wireless network is presented. Then, a distributed mechanism is proposed that can smooth admission control type power control so that every user can stay in the system. Introducing a new externality into utility function, a game theoretic formulation that results in desired distributed actions is made. Next, the proposed externality is investigated in a control theoretic framework. Convergence of gradient based iterative power updates are investigated and stability of corresponding continuous time dynamical system is established. In the final part of the thesis, allocation of buffer space is addressed in a wireless downlink using a queueing theoretic framework. An efficient algorithm that finds optimal buffer partitioning is proposed and applications of the algorithm for different scenarios are illustrated. Implications of the results about cross layer design and multiuser diversity are discussed.

In this study, we considered a heterogeneous, clustered WSN, which consists of two types of nodes (clusterheads and sensor nodes) deployed randomly over a sensing field. We investigated two cases based on how clusterheads can reach the sink: direct and multi-hop communication cases. Network dimensioning problems in randomly deployed WSNs are among the most challenging ones as the attributes of these networks are mostly non-deterministic. We focused on a number of network dimensioning problems based on the connected coverage concept, which is the degree of coverage achieved by only the connected devices. To evaluate connected coverage, we introduced the term cluster size, which is the expected value of the area covered by a clusterhead together with sensor nodes connected to it. We derived formulas for the cluster size and validated them by computer simulations. By using the cluster size formulas, we proposed a method to dimension a WSN for given targeted connected coverage. Furthermore, we formulated cost optimization problems for direct and multi-hop communication cases. These formulations utilize not only cluster size formulas but also the well-connectivity concept. We suggested some search heuristics to solve these optimization problems. Additionally, we justified that, in practical cases, node heterogeneity can provide lower cost solutions. We also investigated the lifetime of WSNs and for mulated a cost optimization problem with connected coverage and lifetime constraints. By solving this optimization problem, one can determine the number of nodes of each type and the initial energies of each type of node that leads to lowest cost solution while satisfying the minimum connected coverage and minimum lifetime requirements.

In this thesis, we consider the problem of decentralized estimation under communication constraints in the context of Collaborative Signal and Information Processing. Motivated by sensor network applications, a high volume of data collected at distinct locations and possibly in diverse modalities together with the spatially distributed nature and the resource limitations of the underlying system are of concern. Designing processing schemes which match the constraints imposed by the system while providing a reasonable accuracy has been a major challenge in which we are particularly interested in the tradeoff between the estimation performance and the utilization of communications subject to energy and bandwidth constraints. One remarkable approach for decentralized inference in sensor networks is to exploit graphical models together with message passing algorithms. In this framework, after the so-called information graph of the problem is constructed, it is mapped onto the underlying network structure which is responsible for delivering the messages in accordance with the schedule of the inference algorithm. However it is challenging to provide a design perspective that addresses the tradeoff between the estimation accuracy and the cost of communications. Another approach has been performing the estimation at a fusion center based on the quantized information provided by the peripherals in which the fusion and quantization rules are sought while taking a restricted set of the communication constraints into account. We consider two classes of in-network processing strategies which cover a broad range of constraints and yield tractable Bayesian risks that capture the cost of communications as well as the penalty for estimation errors. A rigorous design setting is obtained in the form of a constrained optimization problem utilizing the Bayesian risks. These processing schemes have been previously studied together with the structures that the solutions exhibit in the context of decentralized detection in which a decision out of finitely many choices is made. We adopt this framework for the estimation problem. However, for the case, computationally infeasible solutions arise that involve integral operators that are impossible to evaluate exactly in general. In order not to compromise the fidelity of the model we develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both the in-network processing strategies and the solution schemes to the design problem. Doing that, we can produce approximating strategies for decentralized estimation networks under communication constraints captured by the framework including the cost. The proposed Monte Carlo optimization procedures operate in a scalable and efficient manner and can produce results for any family of distributions of concern provided that samples can be produced from the marginals. In addition, this approach enables a quantification of the tradeoff between the estimation accuracy and the cost of communications through a parameterized Bayesian risk.

In this thesis work, automatic recognition algorithms for digital modulated signals are surveyed. Feature extraction and classification algorithm stages are the main parts of a modulation recognition system. Performance of the modulation recognition system mainly depends on the prior knowledge of some of the signal parameters, selection of the key features and classification algorithm selection. Unfortunately, most of the features require some of the signal parameters such as carrier frequency, pulse shape, time of arrival, initial phase, symbol rate, signal to noise ratio, to be known or to be extracted. Thus, in this thesis, features which do not require prior knowledge of the signal parameters, such as the number of the peaks in the envelope histogram and the locations of these peaks, the number of peaks in the frequency histogram, higher order moments of the signal are considered. Particularly, symbol rate and signal to noise ratio estimation methods are surveyed. A method based on the cyclostationarity analysis is used for symbol rate estimation and a method based on the eigenvector decomposition is used for the estimation of signal to noise ratio. Also, estimated signal to noise ratio is used to improve the performance of the classification algorithm. Two methods are proposed for modulation recognition: 1) Decision tree based method 2) Bayesian based classification method A method to estimate the symbol rate and carrier frequency offset of minimum-shift keying (MSK) signal is also investigated.

The first explored area in this thesis is the proposed data hiding method, TCQ-IS. The method is based on Trellis Coded Quantization (TCQ), whose initial state selection is arbitrary. TCQ-IS exploits this fact to hide data. It is a practical multi-dimensional that eliminates the prohibitive task of designing high dimensional quantizers. The strength and weaknesses of the method are stated by various experiments. The second contribution is the proposed data hiding method, Forbidden Zone Data Hiding (FZDH), which relies on the concept of &ldquo / forbidden zone&rdquo / , where host signal is not altered. The main motive of FZDH is to introduce distortion as much as needed, while keeping a range of host signal intact depending on the desired level of robustness. FZDH is compared against Quantization Index Modulation (QIM) as well as DC-QIM and ST-QIM. FZDH outperforms QIM even in 1-D and DC-QIM in higher dimensions. Furthermore, FZDH is comparable with ST-QIM for certain operation regimes. The final contribution is the video data hiding framework that includes FZDH, selective embedding and Repeat Accumulate (RA) codes. De-synchronization due to selective embedding is handled with RA codes. By means of simple rules applied to the embedded frame markers, certain level of robustness against temporal attacks is introduced. Selected coefficients are used to embed message bits by employing multi-dimensional FZDH. The framework is tested with typical broadcast material against common video processing attacks. The results indicate that the framework can be utilized in real life applications.

In this thesis, two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is considered. Usually, DOA estimation is considered in one dimension assuming a fixed elevation angle. While this assumption simplifies the problem, both the azimuth and elevation angles, namely, the 2-D DOA estimates are required in practical scenarios. In this thesis, planar array structures are considered for 2-D DOA estimation. In this context, V-shaped arrays are discussed and some of the important features of these arrays are outlined. A new method for the design of V-shaped arrays is presented for both isotropic and directional beam patterns. The design procedure is simple and can be applied for both uniform and nonuniform V-shaped sensor arrays. Closed form expressions are presented for the V-angle in order to obtain isotropic angle performance. While circular arrays have the isotropic characteristics, V-shaped arrays present certain advantages due to their large aperture for the same number of sensors and inter-sensor distance. The comparison of circular and V-shaped arrays is done by considering the azimuth and elevation Cramer-Rao Bounds (CRB). It is shown that V-shaped and circular arrays have similar characteristics for the sensor position errors while the uniform isotropic (UI) V-array performs better when there is mutual coupling and the sources are correlated. In the literature, there are several techniques for 2-D DOA estimation. Usually, fast algorithms are desired for this purpose since a search in two dimensions is a costly process. These algorithms have a major problem, namely, the pairing of the azimuth-elevation couples for multiple sources. In this thesis, a new fast and effective technique for this purpose is proposed. In this technique, a virtual array output is generated such that when the ESPRIT algorithm is used, the eigenvalues of the rotational transformation matrix have the 2-D angle information in both magnitude and phase. This idea is applied in different scenarios and three methods are presented for these cases. In one case, given an arbitrary array structure, array interpolation is used to generate the appropriate virtual arrays. When the antenna mutual coupling is taken into account, a special type of array structure, such as circular, should be used in order to apply the array interpolation. In general, the array mutual coupling matrix (MCM) should have a symmetric Toeplitz form. It is shown that the 2-D DOA performance of the proposed method approaches to the CRB by using minimum number of antennas in case of mutual coupling. This method does not require the estimation of the mutual coupling coefficients. While this technique is effective, it has problems especially when the number of sources increases. In order to improve the performance, MCM is estimated in the third approach. This new approach performs better, but it cannot be used satisfactorily in case of multipath signals. In this thesis, the proposed idea for fast 2-D DOA estimation is further developed in order to solve the problem when mutual coupling and multipath signals jointly exist. In this case, real arrays with some auxiliary sensors are used to generate a structured mutual coupling matrix. It is shown that the problem can be effectively solved when the array structure has a special form. Specifically, parallel uniform linear arrays (PULA) are employed for this purpose. When auxiliary sensors are used, a symmetric banded Toeplitz MCM is obtained for the PULA. This allows the application of spatial smoothing and ESPRIT algorithm for 2-D DOA estimation. The proposed algorithm uses triplets and presents closed form paired 2-D DOA estimates in case of unknown mutual coupling and multipath signals. Several simulations are done and it is shown that the proposed array structure and the method effectively solve the problem.

Frame synchronization is an important problem in digital communication systems. In frame synchronization, the main task is to find the frame start given the flow of the communication symbols. In this thesis, frame synchronization problem is investigated for both additive white Gaussian noise (AWGN) channels and frequency selective channels. Most of the previous works on frame synchronization consider the simple case of AWGN channels. The algorithms developed for this purpose fail in frequency selective channels. There is limited number of algorithms proposed for the frequency selective channels. In this thesis, existing frame synchronization techniques are investigated for both AWGN and frequency selective channels. Code-aided frame synchronization techniques are combined with the methods for frequency selective channels. Mainly two types of code-aided frame synchronization schemes are considered and two new system structures are proposed for frame synchronization. One of the proposed structures performs better than the alternative methods for frequency selective channels. The overall system for this new synchronizer is composed of a list synchronizer which generates the possible frame starts, a channel estimator, a soft output MLSE equalizer, and a soft output Viterbi decoder. A mode separation algorithm is used to generate the statistics for the selection of the true frame start. Several experiments are done and the performance is outlined for a variety of scenarios.

The phenomenon of fading constitutes a fundamental problem in wireless communications. Researchers have proposed many methods to improve the reliability of communication over wireless channels in the presence of fading. Many studies on this topic have focused on diversity techniques. Transmit diversity is a common diversity type in which multiple antennas are employed at the transmitter. Space-time coding is a technique based on transmit diversity introduced by Tarokh et alii in 1998. In this thesis, various types of space-time codes are examined. Since they were originally introduced in the form of trellis codes, a major part is devoted to space-time trellis codes where the fundamental design criteria are established. Then, space-time block coding, which presents a different approach, is introduced and orthogonal spacetime block codes are analyzed in some detail. Lastly, rank codes from coding theory are studied and their relation to space-time coding are investigated.

Passive source localization is the estimation of the positions of the sources or emitters given the sensor data. In this thesis, some of the well known methods for passive source localization are investigated and compared in a stationary emitter sensor framework. These algorithms are discussed in detail in two and three dimensions for both single and multiple target cases. Passive source localization methods can be divided into two groups as two-step algorithms and single-step algorithms. Angle-of-Arrival (AOA) based Maximum Likelihood (ML) and Least Squares (LS) source localization algorithms, Time- Difference-of-Arrival (TDOA) based ML and LS methods, AOA-TDOA based hybrid ML methods are presented as conventional two step techniques. Direct Position Determination (DPD) method is a well known technique within the single step approaches. In thesis, a number of variants of DPD technique with better computational complexity (the proposed methods do not need eigen-decomposition in the grid search) are presented. These are the Direct Localization (DL) with Multiple Signal Classification (MUSIC), DL with Deterministic ML (DML) and DL with Stochastic ML (SML) methods. The evaluation of these algorithms is done by considering the Cramer Rao Lower Bound (CRLB). Some of the CRLB expressions given in two dimensions in the literature are presented for threedimensions. Extensive simulations are done and the effects of different parameters on the performances of the methods are investigated. It is shown that the performance of the single step algorithms is good even at low SNR. DL with MUSIC algorithm performs as good as the DPD while it has significant savings in computational complexity. AOA, TDOA and hybrid algorithms are compared in different scenarios. It is shown that the improvement achieved by single-step techniques may be acceptable when the system cost and complexity are ignored. The localization algorithms are compared for the multiple target case as well. The effect of sensor deployments on the location performance is investigated.