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<p> In this thesis we develop several approaches to the problem of blind channel equalization based on second-order statistics (808). We consider the single-input singleoutput (8180) system with minimum phase channel where the received signal is sampled at the symbol rate (T-spaced equalizer). We formulate the equalizer design criterion as a simple convex optimization problem, where the equalizer can be obtained efficiently avoiding the local minima problem. </p> <p> We also extend the problem to the single-input multiple-output (8IMO) systems where the received signal is sampled at an integer multiple of the symbol rate. We formulate the problem as a convex optimization problem using the features existing in the channel matrix structure. The problem can be solved efficiently to obtain the equalizer where a global minima is guaranteed. Moreover, we modify this formulation and deduce a closed form solution to the equalizer. Although both methods are sensitive to the channel order as well as existing subspace methods, they perform better than the subspace methods when the channel matrix is close to being singular. Furthermore, we propose an efficient direct minimum mean square error (MM8E) approach to estimate the equalizer. The method does not rely on the channel order and utilizes the channel matrix structure in SIMO systems. Therefore, it outperforms existing algorithms including the previously proposed methods. However, due to the large amount of computations involved in this method we present a new algorithm that belongs to the same class with moderate computational complexity and acceptable performance loss with respect to the latter algorithm. </p> / Thesis / Master of Applied Science (MASc)

In this thesis, we study the effect of channel estimation on the performance of distributed space-time coding (DSTC) in amplify-and-forward (AF) cooperative networks. The relay based transmission takes two phase. In phase I, the source transmits a block of symbols, which include training symbols and data to destination. After receiving signals at relay, the DSTC is adopted to re-encode signals in order to achieve diversity gain at relay nodes. At destination, the signals received in two phase are combined and used to detected data symbols. In the thesis, for AF cooperative networks, the signal received at destination is effected the multiplication of channel coefficients on the source to relay and relay to destination links. Before detection, channel coefficients of all links need to be estimated. We propose a semiblind method to estimate the channel coefficients of direct link and the relay links. The semi-blind channel estimation scheme, exploits a small number of training symbols and second-order statistics of received signals. To improve the detection quality, the channel estimation is modified by treating the detected symbols as extra training symbols. Through simulation, it shows that the proposed channel estimation and the modification leads to obvious performance improvement.

Passive source localization in wireless sensor networks (WSNs) is an important field of research with numerous applications in signal processing and wireless communications. One purpose of a WSN is to determine the position of a signal emitted from a source. This position is estimated based on received noisy measurements from sensors (anchor nodes) that are distributed over a geographical area. In most cases, the sensor positions are assumed to be known exactly, which is not always reasonable. Even if the sensor positions are measured initially, they can change over time. Due to the sensitivity of source location estimation accuracy with respect to the a priori sensor position information, the source location estimates obtained can vary significantly regardless of the localization method used. Therefore, the sensor position uncertainty should be considered to obtain accurate estimates. Among the many localization approaches, signal strength based methods have the advantages of low cost and simple implementation. The received signal energy mainly depends on the transmitted power and path loss exponent which are often unknown in practical scenarios. In this dissertation, three received signal strength difference (RSSD) based methods are presented to localize a source with unknown transmit power. A nonlinear RSSD-based model is formulated for systems perturbed by noise. First, an effective low complexity constrained weighted least squares (CWLS) technique in the presence of sensor uncertainty is derived to obtain a least squares initial estimate (LSIE) of the source location. Then, this estimate is improved using a computationally efficient Newton method. The Cramer-Rao lower bound (CRLB) is derived to determine the effect of sensor location uncertainties on the source location estimate. Results are presented which show that the proposed method achieves the CRLB when the signal to noise ratio (SNR) is sufficiently high. Least squares (LS) based methods are typically used to obtain the location estimate that minimizes the data vector error instead of directly minimizing the unknown parameter estimation error. This can result in poor performance, particularly in noisy environments, due to bias and variance in the location estimate. Thus, an efficient two stage estimator is proposed here. First, a minimax optimization problem is developed to minimize the mean square error (MSE) of the proposed RSSD-based model. Then semidefinite relaxation is employed to transform this nonconvex and nonlinear problem into a convex optimization problem. This can be solved e ciently to obtain the optimal solution of the corresponding semidefinite programming (SDP) problem. Performance results are presented which con rm the e ciency of the proposed method which achieves the CRLB. Finally, an extended total least squares (ETLS) method is developed for blind localization which considers perturbations in the system parameters as well as the constraints imposed by the relation between the observation matrix and data vector. The corresponding nonlinear and nonconvex RSSD-based localization problem is then transformed to an ETLS problem with fewer constraints. This is transformed to a convex semidefinite programming (SDP) problem using relaxation. The proposed ETLS-SDP method is extended to the case with an unknown path loss exponent. The mean squared error (MSE) and corresponding CRLB are derived as performance benchmarks. Performance results are presented which show that the RSSD-based ETLS-SDP method attains the CRLB for a sufficiently large SNR. / Graduate / 0544 / lohrasbi@uvic.ca

Present trends in communication links between devices have opted for wireless instead of wired solutions. As a consequence, unlicensed bands have seen a rise in the interference level as more and more devices are introduced into the market place that take advantage of these free bands for their communication needs. Under these conditions, the receiver's ability to recognize and identify the presence of interference becomes increasingly important. In order for the receiver to make an optimal decision on the signal-of-interest, it has to be aware of the type (modulation) of interference as well as how the received signals are affected (channel) by these impediments in order to appropriately mitigate them. This dissertation addresses the blind (unaided) identification of the signal modulations and the channel in a Multiple Input Multiple Output (MIMO) system. The method presented herein takes advantage of the modulation induced periodicities of the signals in the system and uses higher-order cyclostationary statistics to extract the signal and channel unknowns. This method can be used to identify more signals in the system than antenna elements at the receiver (overloaded case). This dissertation presents a system theoretic analysis of the problem as well as describes the development of an algorithm that can be used in the identification of the channel and the modulation of the signals in the system. Linear and non-linear receivers are examined at the beginning of the manuscript in order to review the a priori information that is needed for each receiver configuration to function properly. / Ph. D.

This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the performance of PSP degrades when certain sequences are transmitted. In this thesis, the blind acquisition characteristics of PSP are discussed. The problematic sequences for any joint ML data and channel estimator are discussed from an analytic perspective. Based on the theory of indistinguishable sequences, modifications to conventional PSP are suggested that improve its acquisition performance significantly. The effect of tree search and list-based algorithms on PSP is also discussed. Proposed improvement techniques are compared for different channels. For higher order channels, complexity issues dominate the choice of algorithms, so PSP with state reduction techniques is considered. Typical misacquisition conditions, transients, and initialization issues are reported. / Master of Science

In this thesis, we proposed a precoding scheme for semi-blind channel estimation in amplify-and-forward (AF) multipath two-way relay networks (TWRN), where two terminals exchange their information through multi-relays. The precoding scheme, which diminishes computational complexity of semi-blind channel estimator, is used to distinguish received signal at both terminals from multi-relays. By applying a non-redundant linear precoding scheme at multi-relays, we proposed a semi-blind channel estimation to estimate the channel impulse response (CIR) of direct link and the cascaded source-relay-terminal links. Firstly, semi-blind channel estimation adopts least-square (LS) estimation to find the CIR of direct link between both terminals using a smaller number of training symbols. Secondly, the CIR of the cascaded source-relay-terminal links are obtained through second-order statistics (SOS) of received signals at both terminals. Consequently, the proposed scheme can effectively reduce the computational complexity and enhance the spectral efficiency in overall system. Simulation results corroborate the effectiveness of the proposed scheme.

This thesis investigates the semi-blind channel estimation in uplink (UL) of Orthogonal Frequency Division Multiple Access (OFDMA) systems based on subspace decomposition. We exploit the orthogonality between signal subspace and noise subspace induced by virtual carriers (VCs) and cyclic prefix (CP) and the property of that the exclusive sub-carriers set is assigned to each user to estimate and identify the channels for each user individually. In OFDMA systems, when some users don¡¦t communicate with base station, the sub-carriers of non-active user provide extra redundancy for channel estimate to enhance the accuracy of channel estimation. Furthermore, the sufficient channel identifiability condition is developed. Furthermore, a novel scheme, called as virtual carriers recovery (VCR) scheme, is proposed to improve the performance of the subspace-based channel estimation method. It suppresses the noise interference by recovering the VCs to zeros at receiver. The simulation results illustrate that the enhancement of VCR scheme is particularly apparent for the partially loaded OFDMA system at low signal to noise ratio (SNR). In addition, the VCR scheme increases the convergence rate of the subspace-base semi-blind channel estimation.

L’estimation de canal joue un rôle important dans les communications mobiles sans fil et en particulier dans les systèmes multi-antennes MIMO. Contrairement aux techniques classiques d’estimation de canal basées sur des séquences d’apprentissage ou des symboles pilotes, les techniques aveugles ne nécessitent aucune insertion de symboles d'apprentissage et permettent d'augmenter le débit utile. Les principales difficultés des techniques aveugles résident dans l’ambiguïté présente sur les estimées. Les techniques d’estimation semi-aveugles, basées sur les mêmes méthodes que l’estimation aveugle, sont plus robustes. Elles exploitent l’information aveugle ainsi que l’information provenant d’un nombre réduit de symboles d’apprentissage. Cette estimation du canal de communication est très utile dans les systèmes MIMO et permet de précoder le signal MIMO-OFDM en lui appliquant un pré-mélange permettant d'améliorer les performances. De nombreux types de précodeurs existent et leurs performances varient en fonction des critères d'optimisation retenus (Water-Filling, MMSE, Equal Error, max-SNR, max-d min …), mais aussi avec la qualité de l'estimation du canal de communication. Nous étudions dans cette thèse l’impact de l’utilisation de l’information du canal (CSI) provenant des méthodes d’estimation aveugle et semi-aveugle, dans l’application des précodeurs linéaires MIMO. Nous présentons également une étude statistique de l’erreur d’estimation provenant de ces méthodes. L’optimisation de ces précodeurs nous mène par la suite à exploiter un autre procédé permettant l’amélioration des performances : les codes correcteurs d’erreur. Nous nous intéressons particulièrement aux codes LDPC non-binaires et leur association avec les précodeurs linéaires MIMO. Nous montrons qu’une adaptation est possible et s’avère bénéfique dans certains cas. L’optimisation de cette association nous a permis de proposer un nouveau précodeur basé sur la maximisation de l’information mutuelle, robuste et plus performant. / Channel estimation plays an important role in wireless mobile communications, especially in MIMO systems. Unlike conventional channel estimation techniques based on training sequences or pilot symbols, blind techniques does not require the insertion of training symbols and allow higher throughput. The main problems of the blind lies in the ambiguity over the estimated channel. Based on the same methods as the blind estimation, the semi-blind estimation techniques are more robust. They exploit the blind information along with information provided by a small number of training symbols. The channel estimation is useful in MIMO systems and allows the precoding of the MIMO-OFDM signal by applying a pre-mixture in order to improve performance. Many types of precoders exist and their performance varies depending not only on the optimization criteria (Water-Filling, MMSE, Equal Error, max-SNR, max-d min ...), but also on the estimated channel. In this thesis we study the impact of using the channel information (CSI) from the blind and semi-blind estimation techniques to apply MIMO linear precoders. We also present a statistical study of the estimation error of these methods. The optimization of these precoders leads eventually to use another process allowing more performance improvement: the error correcting codes. We are particularly interested in non-binary LDPC codes and their association with linear MIMO precoders. We show that a matching is possible, and is beneficial in some cases. The optimization of this combination has allowed us to propose a new robust and more efficient precoder based on the maximization of mutual information.

Orthogonal frequency division multiplexing (OFDM) modulator with redundancy has been adopted in many wireless communication systems for higher data rate transmissions. The introduced redundancy at the transmitter allows us to overcome serious inter-block interference (IBI) problems due to highly dispersive channel. However, the selection of redundancy length will affect the system performance and spectral efficiency, and is highly dependent on the length of channel impulse response. In this thesis, based on the pseudorandom postfix (PRP) OFDM scheme we propose a novel block-based OFDM transceiver framework. Since in the PRP-OFDM system the PRP can be employed for semi-blind channel estimation with order-one statistics of the received signal. Hence, for sufficient redundancy case the PRP-OFDM system with the Bayesian decision feedback equalizer (DFE) is adopted for suppressing the IBI and ISI simultaneously. However, for the insufficient redundancy case (the length of redundancy is less than the order of channel), we first propose a modified scheme for channel estimation. To further reduce the complexity of receiver, the maximum shortening signal-to-noise-ratio time domain equalizer (MSSNR TEQ) with the Bayesian DFE is developed for suppressing the IBI and ISI, separately. That is, after knowing the channel state information (CSI) and removing the effect of IBI with MSSNR TEQ, the Bayesian DFE is applied for eliminating the ISI. Via computer simulation, we verify that performance improvement, in terms of bit error rate (BER), compared with the conventional block-based minimum mean square error (MMSE)-DFE can be achieved.