The full spatial covariance matrix of the multiple input multiple-output (MIMO) channel is an important quantity in channel modeling, communication system signal processing, and performance analysis, and therefore this matrix forms the heart of the research outlined in this dissertation. The work begins with an investigation of a generalized framework for computing the full MIMO spatial covariance based on the power angular spectrum (PAS) of the multipath field and the transmit and receive antenna element radiation patterns. For the case of uniform linear arrays and when the PAS clusters satisfy uniform, truncated Gaussian, or truncated Laplacian distributions, a series expansion is used to allow analytic evaluation of the required integrals in the formulation. The study also demonstrates the validity of some simplifying assumptions used to reduce the complexity of the covariance computation by applying the technique to ray tracing data as well as considers an analysis of the convergence properties of the series when computed using a finite number of terms. The insights and tools obtained from this covariance analysis are then used to develop a general approach for constructing MIMO transmit and receive beamforming vectors based on the full spatial covariance. While transmit and receive beamforming for the MIMO channel is a well-studied topic, when the transmit precoding is based on channel covariance information, developing near-optimal transmit and receive beamformers when the receiver is constrained to use linear processing remains an unsolved problem. This iterative beamforming algorithm presented here can accommodate different types of available channel information and receiver capabilities as well as either a sum power constraint or a per-antenna power constraint. While the latter is more realistic, construction of the optimal transmit precoder is less understood for this constraint. Simulation results based on measured channels demonstrate that the approach generates beamformer solutions whose performance rivals that achieved for an optimal nonlinear receiver architecture.

The objective of this dissertation is to investigate the performance of the recently proposed MIMO technology in real indoor environments based on channel measurements centered at 5.8 GHz. First, a MIMO channel measurement system is implemented based on the virtual antenna array infrastructure. This measurement testbed can acquire the wideband channel matrices of MIMO systems with arbitrary array geometries. The measurement system structure and measurement procedure are described in detail in the first part. The second part is about MIMO channel modeling. Two novel number-of-sources detection algorithms, which are more robust and suitable for practical applications than traditional methods, are proposed. The MIMO path parameters, including delay, DOA, and DOD are estimated from measured data by several estimation schemes based on the ESPRIT algorithm. The accuracies of these estimation schemes are evaluated in terms of the estimation error between the capacities of the directly measured and the reconstructed channels. Moreover, based on ray tracing and measurement results, the spherical wave model is suggested to replace conventional plane wave model in order to prevent the capacity underestimation of short-range MIMO channels. An important observation is that short-range MIMO can achieve full capacity in free space channel. A threshold distance is derived to determine whether the spherical wave model is necessary. In the final part, measurements conducted in the Residential Laboratory are used to investigate the impact of element spacing, LOS, interference, spatial correlation between the interfering and data links, and stream control. A capacity enhancement scheme, which improves the performance by adapting the element locations, is implemented using our measurement system. Finally, the performances of beam selection and antenna selection in combination with MIMO technologies are compared in both narrowband and wideband channels.

No description available.

A new broadband beamformer composed of nested arrays (NAs), multi-dimensional (MD) filters, and multirate techniques is proposed for both linear and planar arrays. It is shown that this combination results in frequency-invariant response. For a given number of sensors, the advantage of using NAs is that the effective aperture for low temporal frequencies is larger than in the case of using uniform arrays. This leads to high spatial selectivity for low frequencies. For a given aperture size, the proposed beamformer can be implemented with significantly fewer sensors and less computation than uniform arrays with a slight deterioration in performance. Taking advantage of the Noble identity and polyphase structures, the proposed method can be efficiently implemented. Simulation results demonstrate the good performance of the proposed beamformer in terms of frequency-invariant response and computational requirements. The broadband beamformer requires a filter bank with a non-compatible set of sampling rates which is challenging to be designed. To address this issue, a filter bank design approach is presented. The approach is based on formulating the design problem as an optimization problem with a performance index which consists of a term depending on perfect reconstruction (PR) and a term depending on the magnitude specifications of the analysis filters. The design objectives are to achieve almost perfect reconstruction (PR) and have the analysis filters satisfying some prescribed frequency specifications. Several design examples are considered to show the satisfactory performance of the proposed method. A new blind multi-stage space-time equalizer (STE) is proposed which can separate narrowband sources from a mixed signal. Neither the direction of arrival (DOA) nor a training sequence is assumed to be available for the receiver. The beamformer and equalizer are jointly updated to combat both co-channel interference (CCI) and inter-symbol interference (ISI) effectively. Using subarray beamformers, the DOA, possibly time-varying, of the captured signal is estimated and tracked. The estimated DOA is used by the beamformer to provide strong CCI cancellation. In order to alleviate inter-stage error propagation significantly, a mean-square-error sorting algorithm is used which assigns detected sources to different stages according to the reconstruction error at different stages. Further, to speed up the convergence, a simple-yet-efficient DOA estimation algorithm is proposed which can provide good initial DOAs for the multi-stage STE. Simulation results illustrate the good performance of the proposed STE and show that it can effectively deal with changing DOAs and time variant channels. / Graduate / 0544 / imanmoaz@uvic.ca

[pt] A estimação de direção de chegada (DoA) é uma importante área de processamento de arranjos de sensores que é encontrada em uma ampla gama de aplicações de engenharia. Este fato, juntamente com o desenvolvimento da área de Compressed Sensing (CS) nos últimos anos, são a principal motivação desta dissertação. Nesta dissertação, é apresentada uma formulação do problema de estimação de direção de chegada como um problema de representação esparsa da sinal e vários algoritmos de recuperação esparsa são derivados e investigados para resolver o problema atual. Os algoritmos propostos são baseados na incorporação da informação prévia sobre o sinal esparso no processo de estimativa. Na primeira parte, nos concentramos no desenvolvimento de dois algoritmos Bayesianos , que se baseiam principalmente no algoritmo iterative hard thresholding (IHT). Devido ao desempenho inferior dos algoritmos convencionais de estimação de chegada em cenários com fontes correlacionadas, nós prestamos atenção especial ao desempenho dos algoritmos propostos nesta condição. Na segunda parte, o problema de otimização baseados na minimização da norma l1 é apresentado e um algoritmo bayesiano é proposto para resolver o problema chamado basis pursuit denoising (BPDN). Os resultados da simulação mostram que os estimadores Bayesianos superam os estimadores não Bayesianos e que a incorporação do conhecimento prévio da distribuição do sinal melhorou substancialmente o desempenho dos algoritmos. / [en] Direction of arrival (DoA) estimation is a key area of sensor array processing which is encountered in a broad range of important engineering applications. This fact together with the development of the Compressed Sensing (CS) area in the last years are the principal motivation of this thesis. In this dissertation, a formulation of the source localization problem as a sparse signal representation problem is presented and several sparse recovery algorithms are derived and investigated for solving the current problem. The proposed algorithms are based on the incorporation of the prior information about the sparse signal in the estimation process. In the first part, we focus on the development of two Bayesian greedy algorithms which are principally based on the iterative hard thresholding (IHT) algorithm. Due to the inferior performance of the conventional DoA estimation algorithm in scenarios with correlated sources, we pay special attention to the performance of the proposed algorithms under this condition. In the second part, the optimization problem using a l1 penalty is introduced and a Bayesian algorithm for solving the basis pursuit denoising problem is presented. Simulation results shows that Bayesian estimators which take into account the prior knowledge of the signal distribution outperform and improve substantially the performance of the non-Bayesian estimators.

Among the research topics needing to be addressed to further the development of phased array feeds (PAFs) for radio astronomical use are challenges associated with calibration, beamforming, and imaging for single dish observations. This dissertation addresses these concerns by providing analysis and solutions that provide a clearer understanding of the effort required to implement PAFs for complex scientific research. It is shown that calibration data are relatively stable over a period of five days and may still be adequate after 70 days. A calibration update system is presented with the potential to refresh old calibrators. Direction-dependent variations have a much greater affect on calibration stability than temporal variations. There is an inherent trade-off in beamformer design between achieving high sensitivity and maintaining beam pattern stability. A hybrid beamformer design is introduced which uses a numerical optimizer to balance the trade-off between these two conflicting goals to provide the greatest sensitivity for a desired amount of pattern control. Relative beam variations that occur when electronically steering beams in the field of view must be reduced in order for a PAF to be useful for source detection and imaging. A dual constraint beamformer is presented that has the ability to simultaneously achieve a uniform main beam gain and specified noise response across all beams. This alone does not reduce the beam variations but it eliminates one aspect of the problem. Incorporating spillover noise control through the use of rim calibrators is shown to reduce the variations between beams. Combining the dual constraint and rim constraint beamformers offers a beamforming option that provides both of these benefits.

In this paper, we analyze the achievable secrecy rates in the two-hop wiretap channel with four nodes, where the transmitter and the receiver have multiple antennas while the relay and the eavesdropper have only a single antenna each. The relay is operating in amplify-and-forward mode and all the channels between the nodes are known perfectly by the transmitter. We discuss different transmission and protection schemes like artificial noise (AN). Furthermore, we introduce interference neutralization (IN) as a new protection scheme. We compare the different schemes regarding the high-SNR slope and the high-SNR power offset and illustrate the performance by simulation results. It is shown analytically as well as by numerical simulations that the high SNR performance of the proposed IN scheme is better than the one of AN.

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cram\'er-Rao lower Bound (CRB) equivalent variance. In the direction of arrival (DOA) scenario, there are different types of prior information; first, we study the case when the location of some of the emitters in the scene is known. We then turn to cases with additional prior information, i.e.~when it is known that some (or all) of the source signals are uncorrelated. As it turns out, knowledge of some DOA combined with this latter form of prior knowledge is especially beneficial, giving estimators that are dramatically more accurate than the state-of-art. We also derive the corresponding CRBs, and show that under quite mild assumptions, the estimators are efficient. Finally, we also investigate the frequency estimation scenario, where the data is a one-dimensional temporal sequence which we model as a spatial multi-sensor response. The line-frequency estimation problem is studied when some of the frequencies are known; through experimental data we show that our approach can be beneficial. The second frequency estimation paper explores the analysis of pulse spin-locking data sequences, which are encountered in nuclear resonance experiments. By introducing a novel modeling technique for such data, we develop a method for estimating the interesting parameters of the model. The technique is significantly faster than previously available methods, and provides accurate estimation results. / Denna doktorsavhandling behandlar parameterestimeringsproblem inom flerkanals-signalbehandling. Den gemensamma förutsättningen för dessa problem är att det finns information om de sökta parametrarna redan innan data analyseras; tanken är att på ett så finurligt sätt som möjligt använda denna kunskap för att förbättra skattningarna av de okända parametrarna. I en uppsats studeras kovariansmatrisskattning när det är känt att den sanna kovariansmatrisen har Kronecker- och Toeplitz-struktur. Baserat på denna kunskap utvecklar vi en metod som säkerställer att även skattningarna har denna struktur, och vi kan visa att den föreslagna skattaren har bättre prestanda än existerande metoder. Vi kan också visa att skattarens varians når Cram\'er-Rao-gränsen (CRB). Vi studerar vidare olika sorters förhandskunskap i riktningsbestämningsscenariot: först i det fall då riktningarna till ett antal av sändarna är kända. Sedan undersöker vi fallet då vi även vet något om kovariansen mellan de mottagna signalerna, nämligen att vissa (eller alla) signaler är okorrelerade. Det visar sig att just kombinationen av förkunskap om både korrelation och riktning är speciellt betydelsefull, och genom att utnyttja denna kunskap på rätt sätt kan vi skapa skattare som är mycket noggrannare än tidigare möjligt. Vi härleder även CRB för fall med denna förhandskunskap, och vi kan visa att de föreslagna skattarna är effektiva. Slutligen behandlar vi även frekvensskattning. I detta problem är data en en-dimensionell temporal sekvens som vi modellerar som en spatiell fler-kanalssignal. Fördelen med denna modelleringsstrategi är att vi kan använda liknande metoder i estimatorerna som vid sensor-signalbehandlingsproblemen. Vi utnyttjar återigen förhandskunskap om källsignalerna: i ett av bidragen är antagandet att vissa frekvenser är kända, och vi modifierar en existerande metod för att ta hänsyn till denna kunskap. Genom att tillämpa den föreslagna metoden på experimentell data visar vi metodens användbarhet. Det andra bidraget inom detta område studerar data som erhålls från exempelvis experiment inom kärnmagnetisk resonans. Vi introducerar en ny modelleringsmetod för sådan data och utvecklar en algoritm för att skatta de önskade parametrarna i denna modell. Vår algoritm är betydligt snabbare än existerande metoder, och skattningarna är tillräckligt noggranna för typiska tillämpningar. / <p>QC 20131115</p>

In this paper, we analyze the achievable secrecy rates in the two-hop wiretap channel with four nodes, where the transmitter and the receiver have multiple antennas while the relay and the eavesdropper have only a single antenna each. The relay is operating in amplify-and-forward mode and all the channels between the nodes are known perfectly by the transmitter. We discuss different transmission and protection schemes like artificial noise (AN). Furthermore, we introduce interference neutralization (IN) as a new protection scheme. We compare the different schemes regarding the high-SNR slope and the high-SNR power offset and illustrate the performance by simulation results. It is shown analytically as well as by numerical simulations that the high SNR performance of the proposed IN scheme is better than the one of AN.

No description available.