The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition produces a reduced rank filter with many benefits, for example, improved performance. This dissertation extends knowledge of the MWF in four areas. The first area is rank and sample support compression. This dissertation examines, under a wide variety of conditions, the size of the adaptive subspace required by the MWF (i.e., the rank) as well as the required number of training samples. Comparisons are made with other algorithms such as the eigenvector-based principal components algorithm. The second area investigated in this dissertation concerns "soft stops", i.e., the insertion of diagonal loading into the MWF. Several methods for inserting loading into the MWF are described, as well as methods for choosing the amount of loading. The next area investigated is MWF rank selection. The MWF will outperform the classical Wiener filter when the rank is properly chosen. This dissertation presents six approaches for selecting MWF rank. The algorithms are compared to one another and an overall design space taxonomy is presented. Finally, as digital modelling capabilities become more sophisticated there is emerging interest in augmenting adaptive processing algorithms to incorporate prior knowledge. This dissertation presents two methods for augmenting the MWF, one based on linear constraints and a second based on non-zero weight vector initialization. Both approaches are evaluated under ideal and perturbed conditions. Together the research described in this dissertation increases the utility and robustness of the multistage Wiener filter. The analysis is presented in the context of adaptive array processing, both spatial array processing and space-time adaptive processing for airborne radar. The results, however, are applicable across the entire spectrum of adaptive signal processing applications. / Ph. D.

There are many novel techniques have been invented to provide high-data rate with high quality communication services for future wireless communications systems. Recently, a novel digital modulation technology for multiple accesses, referred to as the Multi-Carrier Code Division Multiple Access (MC-CDMA), has been proposed to support high data rate transmission; it is based on the combination of CDMA and orthogonal frequency division multiplexing (OFDM). The MC-CDMA has been shown to be an effective technique for combating multipath fading. With MC-CDMA system, a user¡¦s spreading code can be modulated on separate subcarriers, undergo frequency-flat fading channel and offers frequency diversity advantage. But in a multi-user environment, othogonality among spreading codes is severely distorted due to multipath delay spread, such that the system capacity will be limited by the multiple access interferences (MAI). Similar situations exist due to possible narrowband interference (NBI) from other systems. Effective interference reduction will render system capacity to increase, which means interference suppression techniques are vital in improving overall system performance. In this thesis, we propose a new linearly constrained recursive least square algorithm, with diagonal loading approach, referred to as the DL-LC RLS algorithms, to further improve the system performance. The proposed diagonal loading RLS algorithm is different from conventional diagonal loading RLS algorithm, in which the diagonal loading was used to improve the robustness to pointing errors in beamforming problem. However, in this thesis, the diagonal loading approach could be used to alleviate the effect due to multiple access interference (MAI), such that under certain circumstances, better performance could be achieved. Basically, in the proposed algorithm, the power of interference plus noise of received signal will be estimated and subtracted from the diagonal terms of the autocorrelation matrix of received signal. After that instead of using the original autocorrelation matrix, the new correlation matrix, with subtracting power related to the interference plus noise, will be involved during the adaptation processes for updating the weights of the multi-user detector. Finally, computer simulation results, in terms of bit error rate, are used to demonstrate the merits of the proposed scheme compared with the conventional RLS algorithm approach without using the diagonal approaches.

Αντικείμενο της παρούσας διδακτορικής διατριβής είναι η μελέτη και ο σχεδιασμός καινοτόμων αλγορίθμων φυσικού επιπέδου σε ασύρματα συστήματα επικοινωνίας που χρησιμοποιούν διαμόρφωση με πολύπλεξη συχνότητας ορθογωνίων φερουσών (Orthogonal Frequency Division Multiplexing - OFDM). Η έρευνα επικεντρώθηκε σε δύο κατηγορίες προβλημάτων, στον σχεδιασμό αλγορίθμων προσαρμοστικής διαμόρφωσης καθώς και αλγορίθμων αντιμετώπισης ισχυρού θορύβου φάσης. Αναπτύχθηκαν αλγόριθμοι εκτίμησης φάσης με γραμμική πολυπλοκότητα, μέσω ενός καινούργιου εναλλακτικού μοντέλου περιγραφής του συστήματος. Το μοντέλο αυτό επιτρέπει την επέκταση των κλασικών αλγορίθμων εκτίμησης της κοινής φάσης με στόχο την εκτίμηση του συνολικού διανύσματος θορύβου φάσης. Επιπλέον, η τεχνική διαγώνιας φόρτωσης (diagonal-loading) προσαρμόστηκε κατάλληλα για τη βελτίωση σύγκλισης της προτεινόμενης λύσης. Τέλος, προτάθηκε και αξιολογήθηκε ένα συνολικό σύστημα OFDM όπου η εκτίμηση του καναλιού, της διαταραχής φάσης και των δεδομένων βασίζονται στο κριτήριο ελαχίστων τετραγώνων, διατηρώντας έτσι τη συνολική πολυπλοκότητα σε χαμηλά επίπεδα. Στο πλαίσιο του σχεδιασμού αλγορίθμων προσαρμοστικής διαμόρφωσης προτείνεται ένα γενικό μοντέλο περιγραφής απόδοσης συστήματος ικανό να περιγράψει τα αναπτυσσόμενα πρωτόκολλα μετάδοσης. Η πρόταση αυτή εντάσσεται στην οικογένεια των τεχνικών ισοδύναμης σηματοθορυβικής απεικόνισης (Εffective SNR Μapping - ESM). Χρησιμοποιώντας τις τεχνικές ESM και κατάλληλους περιορισμούς στην παραμετροποίηση των μεταβλητών μετάδοσης, αναπτύχθηκαν αλγόριθμοι προσαρμοστικής διαμόρφωσης χαμηλής πολυπλοκότητας που ικανοποιούν διαφορετικά κριτήρια βελτιστοποίησης. Επιπρόσθετα, προτείνεται ένα γενικό πλαίσιο για τον σχεδιασμό αλγορίθμων προσαρμοστικής διαμόρφωσης, χρησιμοποιώντας προσεγγιστικά μοντέλα απόδοσης. Ορίστηκαν οι κατάλληλες μετρικές για την ποσοτικοποίηση της σπατάλης ενέργειας που επιφέρει η χρήση προσεγγιστικών μοντέλων. Μελετήθηκε η επίδραση της καθυστέρησης ανατροφοδότησης πληροφορίας καναλιού στους αλγορίθμους και παρήχθησαν κατάλληλα μοντέλα περιγραφής απόδοσης που συμπεριλαμβάνουν το χρόνο καθυστέρησης. Το συνολικό αποτέλεσμα της εργασίας είναι αλγόριθμοι που καταφέρνουν υψηλή απόδοση συστήματος, με χαμηλή πολυπλοκότητα, κάτι το οποίο τους κάνει υλοποιήσιμους σε ρεαλιστικά συστήματα. / The objective of this thesis is to study and develop novel, low complexity physical layer algorithms for Orthogonal Frequency Division Multiplexing (OFDM) based communication systems. The study aims at two algorithmic categories, namely adaptive modulation and coding and compensation of severe phase noise (PHN) errors. A parameterized windowed least-squares (WLS) decision directed phase error estimator is proposed via proper (alternative) system modeling, applied to both channel estimation and data detection stage in OFDM systems. The window is optimized so as to minimize the post-compensation error variance (PCEV) of the residual phase, analytically computed for arbitrary PHN and frequency offset (FO) models. Closed-form expressions for near-optimal windows are derived for zero-mean FO, Wiener and first-order autoregressive PHN models, respectively. Furthermore, the diagonal-loading approach is properly employed, initially proposed for providing robustness to a general class of estimators in the presence of model mismatch, to enhance convergence of the iterative estimation scheme, in those high-SNR regions where the effect of data decision errors dominates performance. In the proposed OFDM scheme, channel, IFO estimation and data equalization are also based on the LS criterion, thus keeping the overall system complexity low. A generic performance description model is proposed and used for AMC algorithmic design, capable of describing most of current and under preparation communication protocols. This model proposition is incorporated to a larger family of performance modelling techniques named Effective SNR Mapping techniques (ESM). Using the ESM techniques and proper parameter adaptation constraints, a number of low-complexity AMC algorithms are developed under a chosen set of optimization scenarios. A framework for the design of AMC algorithms using approximate performance description models is proposed. Specific bounds are derived for quantifying the power loss when using approximate models. The effect of outdated channel state information is also studied by statistically characterizing the effective SNR at the receiver. This description allows parameter adaptation under mobility scenarios. The main value of this collective procedure is the development of low complexity- high performance algorithms, implementable on pragmatic OFDM systems.