MIMO signal processing in offset-QAM based filter bank multicarrier systems

Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft

Blind identification of acoustic systems and enhancement of reverberant speech

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System Identification with Applications in Speech Enhancement

As the increasing popularity of integrating hands-free telephony on mobile portable devices and the rapid development of voice over internet protocol, identification of acoustic systems has become desirable for compensating distortions introduced to speech signals during transmission, and hence enhancing the speech quality. The objective of this research is to develop system identification algorithms for speech enhancement applications including network echo cancellation and speech dereverberation. A supervised adaptive algorithm for sparse system identification is developed for network echo cancellation. Based on the framework of selective-tap updating scheme on the normalized least mean squares algorithm, the MMax and sparse partial update tap-selection strategies are exploited in the frequency domain to achieve fast convergence performance with low computational complexity. Through demonstrating how the sparseness of the network impulse response varies in the transformed domain, the multidelay filtering structure is incorporated to reduce the algorithmic delay. Blind identification of SIMO acoustic systems for speech dereverberation in the presence of common zeros is then investigated. First, the problem of common zeros is defined and extended to include the presence of near-common zeros. Two clustering algorithms are developed to quantify the number of these zeros so as to facilitate the study of their effect on blind system identification and speech dereverberation. To mitigate such effect, two algorithms are developed where the two-stage algorithm based on channel decomposition identifies common and non-common zeros sequentially; and the forced spectral diversity approach combines spectral shaping filters and channel undermodelling for deriving a modified system that leads to an improved dereverberation performance. Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased dereverberation techniques. Comprehensive simulations and discussions demonstrate the effectiveness of the aforementioned algorithms. A discussion on possible directions of prospective research on system identification techniques concludes this thesis

Optimal channel equalization for filterbank transceivers in presence of white noise

Filterbank transceivers are widely employed in data communication networks to cope with inter-symbol-interference (ISI) through the use of redundancies. This dissertation studies the design of the optimal channel equalizer for both time-invariant and time-varying channels, and wide-sense stationary (WSS) and possible non-stationary white noise processes. Channel equalization is investigated via the filterbank transceivers approach. All perfect reconstruction (PR) or zero-forcing (ZF) receiver filterbanks are parameterized in an affine form, which eliminate completely the ISI. The optimal channel equalizer is designed through minimization of the mean-squared-error (MSE) between the detected signals and the transmitted signals. Our main results show that the optimal channel equalizer has the form of state estimators, and is a modified Kalman filter. The results in this dissertation are applicable to discrete wavelet multitone (DWMT) systems, multirate transmultiplexers, orthogonal frequency division multiplexing (OFDM), and direct-sequence/spread-spectrum (DS/SS) based code division multiple access (CDMA) networks. Design algorithms for the optimal channel equalizers are developed for different channel models, and white noise processes, and simulation examples are worked out to illustrate the proposed design algorithms

Underwater communication via particle velocity channels : principles, channel models, and system design

A vector sensor is capable of measuring important non-scalar components of the acoustic field such as the particle velocity, which cannot be obtained by a single scalar pressure sensor. In the past few decades, extensive research has been conducted on the theory and design of vector sensors. On the other hand, underwater acoustic communication systems have been relying on scalar sensors only, which measure the pressure of the acoustic field. By taking advantage of the vector components of the acoustic field, such as the particle velocity, the vector sensor can be used for detecting the transmitted data. In this dissertation, the concept of data detection and equalization in underwater particle velocity channels using acoustic vector sensors was developed. System equations for such a receiver were derived and channel equalization using these sensors was formulated. A multiuser system using vector sensors and space time block codes was also developed, which does not use spreading codes and bandwidth expansion. This is particularly important in bandlimited underwater channels. With regard to channel models for particle velocity channels, characterization of particle velocity channels and their impact on vector sensor communication systems performance were therefore of interest. In multipath channels such as shallow waters, a vector sensor receives the signal through several paths and each path has a different delay (travel time). Motion of the transmitter or receiver in a multipath channel introduces different Doppler shifts as well. Those introduce different levels of correlation in an array of vector sensors. Therefore, in this dissertation, a statistical framework for mathematical characterization of different types of correlations in acoustic vector sensor arrays was developed. Exact and closed-form approximation correlation expressions were derived which related signal correlations to some key channel parameters such as mean angle of arrivals and angle spreads. Using these expressions, the correlations between the pressure and velocity channels of the sensors could be calculated, in terms of element spacing, frequency and time separation. The derived closed-form parametric expressions for the signal correlations can serve as useful tools to estimate some important physical parameters as well. Knowledge of the delay and Doppler spreads in acoustic particle velocity channel is also important for efficient design of underwater vector sensor communication system. In this dissertation, these channel spreads were characterized using the zero crossing rates of channel responses in frequency and time domain. Useful expressions for delay and Doppler spreads were derived in terms of the key channel parameters, mean angle of arrivals and angle spreads. These results are needed for design and performance predication of communication systems in time-varying and frequency-selective underwater particle velocity channels

Design of optimal equalizers and precoders for MIMO channels

Channel equalization has been extensively studied as a method of combating ISI and ICI for high speed MIMO data communication systems. This dissertation focuses on optimal channel equalization in the presence of non-white observation noises with unknown PSD but bounded power-norm. A worst-case approach to optimal design of channel equalizers leads to an equivalent optimal H-infinity filtering problem for the MIMO communication systems. An explicit design algorithm is derived which not only achieves the zero-forcing (ZF) condition, but also minimizes the RMS error between the transmitted symbols and the received symbols. The second part of this dissertation investigates the design of optimal precoders which minimize the bit error rate (BER) subject to a fixed transmit-power constraint for the multiple antennas downlink communication channels under the perfect reconstruction (PR) condition. The closed form solutions are derived and an efficient design algorithm is proposed. The performance evaluations indicate that the optimal precoder design for multiple antennas communication systems proposed herein is an attractive/reasonable alternative to the existing precoder design techniques

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Equalization of CPM signals over doubly-selective aeronautical channels

Communication technologies have always been one of the fundamental milestones of the aeronautical environment. Despite the growing demand for high performances, the aviation context is reluctant to move towards new technologies. Common communication strategies are not suitable to transmit at very high data rates over time- and/or frequency-dispersive (i.e., doubly-selective) air-ground channels, therefore, new requirements have to be fulfilled by an incremental approach, that is, by updating some parts of the legacy systems. This thesis deals with receiver synthesis for aeronautical communication data-links employing continuous-phase modulated (CPM) signals over doubly-selective wireless communication channels. The goal is to design efficient and low-complexity time-varying equalizers, by exploiting all of the CPM signal features, in order to compensate for the effects due to the rapidly time-varying aeronautical channels. The application of the basis expansion model (BEM) to a typical aeronautical communication channel is considered and validated by computer simulations. The second-order statistical characterization of the pseudo-symbols arising from Laurent representation of CPM signals is introduced and discussed. Both linear time-varying (LTV) and widely-linear time-varying (WLTV) zero forcing (ZF) and minimum mean square error (MMSE) receiver structures for CPM signals operating over doubly-selective channels are proposed and implemented by using the BEM model for the channel. Monte Carlo simulation results, carried out in typical aeronautical scenarios, show that the proposed approaches are able to work satisfactorily also over rapidly time-varying channels