Signal processing for future MIMO-OFDM wireless communication systems

The combination of multiple-input multiple-output (MIMO) technology and orthogonal frequency division multiplexing (OFDM) is likely to provide the air-interface solution for future broadband wireless systems. A major challenge for MIMO-OFDM systems is the problem of multi-access interference (MAI) induced by the presence of multiple users transmitting over the same bandwidth. Novel signal processing techniques are therefore required to mitigate MAI and thereby increase link performance. A background review of space-time block codes (STBCs) to lever age diversity gain in MIMO systems is provided together with an introduction to OFDM. The link performance of an OFDM system is also shown to be sensitive to time-variation of the channel. Iterative minimum mean square error (MMSE) receivers are therefore proposed to overcome such time-variation. In the context of synchronous uplink transmission, a new two-step hard-decision interference cancellation receiver for STBC MIMO-OFDM is shown to have robust performance and relatively low complexity. Further improvement is obtained through employing error control coding methods and iterative algorithms. A soft output multiuser detector based on MMSE interference suppression and error correction coding at the first stage is shown by frame error rate simulations to provide significant performance improvement over the classical linear scheme. Finally, building on the "turbo principle", a low-complexity iterative interference cancellation and detection scheme is designed to provide a good compromise between the exponential computational complexity of the soft interference cancellation linear MMSE algorithm and the near-capacity performance of a scheme which uses iterative turbo processing for soft interference suppression in combination with multiuser detection

Transceiver Design with Iterative Decoding of Capacity-Approaching codes over Fading channels

Ph.DDOCTOR OF PHILOSOPH

Reduced Receivers for Faster-than-Nyquist Signaling and General Linear Channels

Fast and reliable data transmission together with high bandwidth efficiency are important design aspects in a modern digital communication system. Many different approaches exist but in this thesis bandwidth efficiency is obtained by increasing the data transmission rate with the faster-than-Nyquist (FTN) framework while keeping a fixed power spectral density (PSD). In FTN consecutive information carrying symbols can overlap in time and in that way introduce a controlled amount of intentional intersymbol interference (ISI). This technique was introduced already in 1975 by Mazo and has since then been extended in many directions. Since the ISI stemming from practical FTN signaling can be of significant duration, optimum detection with traditional methods is often prohibitively complex, and alternative equalization methods with acceptable complexity-performance tradeoffs are needed. The key objective of this thesis is therefore to design reduced-complexity receivers for FTN and general linear channels that achieve optimal or near-optimal performance. Although the performance of a detector can be measured by several means, this thesis is restricted to bit error rate (BER) and mutual information results. FTN signaling is applied in two ways: As a separate uncoded narrowband communication system or in a coded scenario consisting of a convolutional encoder, interleaver and the inner ISI mechanism in serial concatenation. Turbo equalization where soft information in the form of log likelihood ratios (LLRs) is exchanged between the equalizer and the decoder is a commonly used decoding technique for coded FTN signals. The first part of the thesis considers receivers and arising stability problems when working within the white noise constraint. New M-BCJR algorithms for turbo equalization are proposed and compared to reduced-trellis VA and BCJR benchmarks based on an offset label idea. By adding a third low-complexity M-BCJR recursion, LLR quality is improved for practical values of M. M here measures the reduced number of BCJR computations for each data symbol. An improvement of the minimum phase conversion that sharpens the focus of the ISI model energy is proposed. When combined with a delayed and slightly mismatched receiver, the decoding allows a smaller M without significant loss in BER. The second part analyzes the effect of the internal metric calculations on the performance of Forney- and Ungerboeck-based reduced-complexity equalizers of the M-algorithm type for both ISI and multiple-input multiple-output (MIMO) channels. Even though the final output of a full-complexity equalizer is identical for both models, the internal metric calculations are in general different. Hence, suboptimum methods need not produce the same final output. Additionally, new models working in between the two extremes are proposed and evaluated. Note that the choice of observation model does not impact the detection complexity as the underlying algorithm is unaltered. The last part of the thesis is devoted to a different complexity reducing approach. Optimal channel shortening detectors for linear channels are optimized from an information theoretical perspective. The achievable information rates of the shortened models as well as closed form expressions for all components of the optimal detector of the class are derived. The framework used in this thesis is more general than what has been previously used within the area

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Reduced-complexity signal processing techniques for multiple-input multiple-output storage and wireless communication systems

Ph.DNUS-TU/E JOINT PH.D. PROGRAMM

Super-orthogonal space-time turbo codes in Rayleigh fading channels.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2005.The vision of anytime, anywhere communications coupled by the rapid growth of wireless subscribers and increased volumes of internet users, suggests that the widespread demand for always-on access data, is sure to be a major driver for the wireless industry in the years to come. Among many cutting edge wireless technologies, a new class of transmission techniques, known as Multiple-Input Multiple-Output (MIMO) techniques, has emerged as an important technology leading to promising link capacity gains of several fold increase in data rates and spectral efficiency. While the use of MIMO techniques in the third generation (3G) standards is minimal, it is anticipated that these technologies will play an important role in the physical layer of fixed and fourth generation (4G) wireless systems. Concatenated codes, a class of forward error correction codes, of which Turbo codes are a classical example, have been shown to achieve reliable performance which approach the Shannon limit. An effective and practical way to approach the capacity of MIMO wireless channels is to employ space-time coding (STC). Space-Time coding is based on introducing joint correlation in transmitted signals in both the space and time domains. Space-Time Trellis Codes (STTCs) have been shown to provide the best trade-off in terms of coding gain advantage, improved data rates and computational complexity. Super-Orthogonal Space-Time Trellis Coding (SOSTTC) is the recently proposed form of space-time trellis coding which outperforms its predecessor. The code has a systematic design method to maximize the coding gain for a given rate, constellation size, and number of states. Simulation and analytical results are provided to justify the improved performance. The main focus of this dissertation is on STTCs, SOSTTCs and their concatenated versions in quasi-static and rapid Rayleigh fading channels. Turbo codes and space-time codes have made significant impact in terms of the theory and practice by closing the gap on the Shannon limit and the large capacity gains provided by the MIMO channel, respectively. However, a convincing solution to exploit the capabilities provided by a MIMO channel would be to build the turbo processing principle into the design of MIMO architectures. The field of concatenated STTCs has already received much attention and has shown improved performance over conventional STTCs. Recently simple and double concatenated STTCs structures have shown to provide a further improvement performance. Motivated by this fact, two concatenated SOSTTC structures are proposed called Super-orthogonal space-time turbo codes. The performance of these new concatenated SOSTTC is compared with that of concatenated STTCs and conventional SOSTTCs with simulations in Rayleigh fading channels. It is seen that the SOST-CC system outperforms the ST-CC system in rapid fading channels, whereas it maintains performance similar to that in quasi-static. The SOST-SC system has improved performance for larger frame lengths and overall maintains similar performance with ST-SC systems. A further investigation of these codes with channel estimation errors is also provided