Low-complexity iterative receiver algorithms for multiple-input multiple-output underwater wireless communications

This dissertation proposes three low-complexity iterative receiver algorithms for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First is a bidirectional soft-decision feedback Turbo equalizer (Bi-SDFE) which harvests the time-reverse diversity in severe multipath MIMO channels. The Bi-SDFE outperforms the original soft-decision feedback Turbo equalizer (SDFE) while keeping its total computational complexity similar to that of the SDFE. Second, this dissertation proposes an efficient direct adaptation Turbo equalizer for MIMO UWA communications. Benefiting from the usage of soft-decision reference symbols for parameter adaptation as well as the iterative processing inside the adaptive equalizer, the proposed algorithm is efficient in four aspects: robust performance in tough channels, high spectral efficiency with short training overhead, time efficient with fast convergence and low complexity in hardware implementation. Third, a frequency-domain soft-decision block iterative equalizer combined with iterative channel estimation is proposed for the uncoded single carrier MIMO systems with high data efficiency. All the three new algorithms are evaluated by data recorded in real world ocean experiment or pool experiment. Finally, this dissertation also compares several Turbo equalizers in single-input single-output (SISO) UWA channels. Experimental results show that the channel estimation based Turbo equalizers are robust in SISO underwater transmission under harsh channel conditions --Abstract, page iv

doi:10.1155/2011/614571 Research Article MMSE Beamforming for SC-FDMA Transmission over

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We consider transmit beamforming for single-carrier frequency-division multiple access (SC-FDMA) transmission over frequency-selective multiple-input multiple-output (MIMO) channels. The beamforming filters are optimized for minimization of the sum of the mean-squared errors (MSEs) of the transmitted data streams after MIMO minimum mean-squared error linear equalization (MMSE-LE), and for minimization of the product of the MSEs after MIMO MMSE decision-feedback equalization (MMSE-DFE), respectively. We prove that for SC-FDMA transmission in both cases eigenbeamforming, diagonalizing the overall channel, together with a nonuniform power distribution is the optimum beamforming strategy. The optimum power allocation derived for MMSE-LE is similar in spirit to classical results for the optimum continuous-time transmit filter for linear modulation formats obtained by Berger/Tufts and Yang/Roy, whereas for MMSE-DFE the capacity achieving waterfilling strategy well known from conventional single-carrier transmission schemes is obtained. Moreover, we present a modification of the beamformer design to mitigate an increase of the peak-to-average power ratio (PAPR) which is in general associated with beamforming. Simulation results demonstrate the high performance of the proposed beamforming algorithms. 1

Channel modeling, estimation and equalization in wireless communication

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (May 25, 2006)Includes bibliographical references.Vita.Thesis (Ph. D.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.Channel modeling, estimation and equalization are discussed throughout this dissertation. Relevant research topics are first studied at the beginning of each chapter and the new methods are proposed to improve the system performance. MLSE is an optimum equalizer for all the case. However, due to its computational complexity, it is impractical for today technologies in third generation wireless communication. Thus, a suboptimum equalizer so-called perturbation equalizer is proposed, which outperforms the RSSE equalizer in the sense of bit error rate or computational complexity. In order to improve the system performance dramatically, the iterative equalization algorithm is implemented. It has been shown that the turbo equalization using the trellis based Maximum A Posteriori equalizer is a powerful receiver that yielding the optimum system performance. Unfortunately, due to its exhausted computational complexity, a suboptimal equalizer is required. An improved DFE algorithm, which only requires low computational complexity, is proposed for turbo equalization. The promising simulation results indicate that the proposed equalizer provides significant improvement in bit error rate while compared to the conventional DFE algorithm. Prior to channel equalization, channel estimation enable us to extract the necessary channel information from the pilot symbols for equalizers. Least-squares algorithm is a promising estimation algorithm providing the channel is time-invariant in a given period. Based on the derivations, we show that the channel is no longer constant and a new least-squares based algorithm is proposed to estimate the channel accurately. Simulation results convince us that the new algorithm provides the equalizer more reliable information. Besides, antenna diversity is another promising technique implemented practically to improve the system performance provided that the channels of antennas are not correlated. A new three dimensional multiple-input multiple-output abstract model is proposed for the investigation and understanding of the correlation of fading channel. The new model allows us to consider the channel correlation of which the mobile stations receive the incoming waves from any directions and angle spreads. Based on this abstract model, the closed form and mathematical tractable formula is derived for space-time correlation function. The new function can be further simplified other known special cases

Transmissibility operators for state and output estimation in nonlinear systems

Transmissibility operators are mathematical objects that characterize the relationship between two subsets of responses of an underlying system. The importance of transmissiblity operators comes from the fact that these operators are independent on the system inputs. This work develops the transmissibility theory for nonlinear systems for the first time. The system nonlinearities are assumed to be unknown, which gives a wide range of possible engineering applications in different disciplines. Four different methods are developed to deal with these nonlinearities. The first method is by re-constructing the system nonlinearities as independent excitations on the system. This method handles the inherent unmodeled nonlinearities within the system. The second method is by designing a transmissibility-based sliding mode control. This method rejects unwanted nonlinearities such as system faults. The third method is by constructing the system as time-variant linear system, and use recursive least squares to solve it. This method can handle nonlinear systems with time-variant dynamics. The fourth method is by designing a new robust estimation technique called high-gain transmissibility (HGT) that is inspired by high-gain observers. This estimator has the ability to robustly estimate the system states in a high-gain form. The majority of modern fault detection, control systems, and robots localization depend on mathematically estimating the system states and outputs. Transmissibility-based estimation is incorporated in this work with these three theoretical applications. For fault detection, transmissibility operators are used along a set of outputs to estimate the measurements of another set of outputs. Then faults are detected by comparing the estimated and measured outputs with each other. Control approaches use the transmissibility-based estimation to construct the control signal, in which is injected back to the original system. Robots localization fuses the transmissibility-based estimation with real-time sensor measurements to minimize the error in determining the robot displacements. These three theoretical applications are applied on four different systems. The first system is Connected Autonomous Vehicles (CAV) platoons. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Transmissibilities are proposed along with the measurements from sensors available in CAV platoons to identify transmissibility operators. This will be then developed to mixed autonomous and human-driven vehicle platoons. Besides the wide range of physical and cyber faults in such systems, this is also motivated by the fact that on-road human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators are used here to handle both cyber-physical faults as well as the human-drivers’ behaviour. The platoon faults are then proposed to be mitigated using a transmissibility-based sliding mode controller. Moreover, transmissibilities are integrated with Kalman filter to localize CAV platoons while operating under non-Gaussian environment as unknown nonlinearities. The second system is a multi-actuator micro positioning system that is used in the semi-conductors industry. Transmissibility operators are applied on this system for fault detection and fault-tolerant control. Fault detection is represented in applying the proposed developments to actuator fault detection. Some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges will be considered. Transmissibilities then will be used for fault detection without knowledge of the dynamics of the system or the excitation that acts on the system. Next, a transmissibility-based sliding mode control will be implemented to mitigate common actuator faults in multi-actuator systems. The third system is flexible structures subjected to unknown and random excitations. Structures used in applications subjected to turbulent fluid flow such as aerospace and underwater applications are subjected to random excitations distributed along the structure. Transmissibility operators are used for the purpose of structural fault detection and localization during the system operation. The fourth system is robotic manipulators with bounded nonlinearities and time-variant parameters. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are integrated with Recursive Least Squares (RLS) to overcome the unknown variant parameters. RLS identifies transmissibilities used in the structure of noncausal FIR (Finite Impulse Response) models. While parameter variation can be treated as system nonlinearities, the RLS algorithm is used to optimize what time-variant dynamics to include in the transmissibility operator and what dynamics to push to the system nonlinearities over time. The identified transmissibilities are then used for the purpose of fault detection in an experimental robotic arm with variant picked mass

Towards low-cost gigabit wireless systems at 60 GHz

The world-wide availability of the huge amount of license-free spectral space in the 60 GHz band provides wide room for gigabit-per-second (Gb/s) wireless applications. A commercial (read: low-cost) 60-GHz transceiver will, however, provide limited system performance due to the stringent link budget and the substantial RF imperfections. The work presented in this thesis is intended to support the design of low-cost 60-GHz transceivers for Gb/s transmission over short distances (a few meters). Typical applications are the transfer of high-definition streaming video and high-speed download. The presented work comprises research into the characteristics of typical 60-GHz channels, the evaluation of the transmission quality as well as the development of suitable baseband algorithms. This can be summarized as follows. In the first part, the characteristics of the wave propagation at 60 GHz are charted out by means of channel measurements and ray-tracing simulations for both narrow-beam and omni-directional configurations. Both line-of-sight (LOS) and non-line-of-sight (NLOS) are considered. This study reveals that antennas that produce a narrow beam can be used to boost the received power by tens of dBs when compared with omnidirectional configurations. Meanwhile, the time-domain dispersion of the channel is reduced to the order of nanoseconds, which facilitates Gb/s data transmission over 60-GHz channels considerably. Besides the execution of measurements and simulations, the influence of antenna radiation patterns is analyzed theoretically. It is indicated to what extent the signal-to-noise ratio, Rician-K factor and channel dispersion are improved by application of narrow-beam antennas and to what extent these parameters will be influenced by beam pointing errors. From both experimental and analytical work it can be concluded that the problem of the stringent link-budget can be solved effectively by application of beam-steering techniques. The second part treats wideband transmission methods and relevant baseband algorithms. The considered schemes include orthogonal frequency division multiplexing (OFDM), multi-carrier code division multiple access (MC-CDMA) and single carrier with frequency-domain equalization (SC-FDE), which are promising candidates for Gb/s wireless transmission. In particular, the optimal linear equalization in the frei quency domain and associated implementation issues such as synchronization and channel estimation are examined. Bit error rate (BER) expressions are derived to evaluate the transmission performance. Besides the linear equalization techniques, a low-complexity inter-symbol interference cancellation technique is proposed to achieve much better performance of code-spreading systems such as MC-CDMA and SC-FDE. Both theoretical analysis and simulations demonstrate that the proposed scheme offers great advantages as regards both complexity and performance. This makes it particularly suitable for 60-GHz applications in multipath environments. The third part treats the influence of quantization and RF imperfections on the considered transmission methods in the context of 60-GHz radios. First, expressions for the BER are derived and the influence of nonlinear distortions caused by the digital-to-analog converters, analog-to-digital converters and power amplifiers on the BER performance is examined. Next, the BER performance under the influence of phase noise and IQ imbalance is evaluated for the case that digital compensation techniques are applied in the receiver as well as for the case that such techniques are not applied. Finally, a baseline design of a low-cost Gb/s 60-GHz transceiver is presented. It is shown that, by application of beam-steering in combination with SC-FDE without advanced channel coding, a data rate in the order of 2 Gb/s can be achieved over a distance of 10 meters in a typical NLOS indoor scenario

Sparse nonlinear optimization for signal processing and communications

This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively. For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms. For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization. For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv

A Framework for Low-Complexity Iterative Interference Cancellation in Communication Systems

Thesis Supervisor: Gregory W. Wornell Title: ProfessorCommunication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter precoding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterateddecision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved precoding, iterated-decision detectors asymptotically achieve the performance ofML detection (and thus the interferencefree lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with modeinterleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information ratesHewlett-Packard under the MIT/HPAlliance, the National Science Foundation, the Semiconductor Research Corporation, Texas Instruments through the Leadership Universities Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship Program

A framework for low-complexity iterative interference cancellation in communication systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 211-215).Communication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter preceding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterated-decision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol(cont.) decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved preceding, iterated-decision detectors asyrmptotically achieve the performance of ML detection (and thus the interference-free lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with mode-interleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information rates.by Albert M. Chan.Ph.D