Systematic redundant residue number system codes: analytical upper bound and iterative decoding performance over AWGN and Rayleigh channels

The novel family of redundant residue number system (RRNS) codes is studied. RRNS codes constitute maximum–minimum distance block codes, exhibiting identical distance properties to Reed–Solomon codes. Binary to RRNS symbol-mapping methods are proposed, in order to implement both systematic and nonsystematic RRNS codes. Furthermore, the upper-bound performance of systematic RRNS codes is investigated, when maximum-likelihood (ML) soft decoding is invoked. The classic Chase algorithm achieving near-ML soft decoding is introduced for the first time for RRNS codes, in order to decrease the complexity of the ML soft decoding. Furthermore, the modified Chase algorithm is employed to accept soft inputs, as well as to provide soft outputs, assisting in the turbo decoding of RRNS codes by using the soft-input/soft-output Chase algorithm. Index Terms—Redundant residue number system (RRNS), residue number system (RNS), turbo detection

Residue number system coded differential space-time-frequency coding.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2007.The rapidly growing need for fast and reliable transmission over a wireless channel motivates the development of communication systems that can support high data rates at low complexity. Achieving reliable communication over a wireless channel is a challenging task largely due to the possibility of multipaths which may lead to intersymbol interference (ISI). Diversity techniques such as time, frequency and space are commonly used to combat multipath fading. Classical diversity techniques use repetition codes such that the information is replicated and transmitted over several channels that are sufficiently spaced. In fading channels, the performance across some diversity branches may be excessively attenuated, making throughput unacceptably small. In principle, more powerful coding techniques can be used to maximize the diversity order. This leads to bandwidth expansion or increased transmission power to accommodate the redundant bits. Hence there is need for coding and modulation schemes that provide low error rate performance in a bandwidth efficient manner. If diversity schemes are combined, more independent dimensions become available for information transfer. The first part of the thesis addresses achieving temporal diversity through employing error correcting coding schemes combined with interleaving. Noncoherent differential modulation does not require explicit knowledge or estimate of the channel, instead the information is encoded in the transitions. This lends itself to the possibility of turbo-like serial concatenation of a standard outer channel encoder with an inner modulation code amenable to noncoherent detection through an interleaver. An iterative approach to joint decoding and demodulation can be realized by exchanging soft information between the decoder and the demodulator. This has been shown to be effective and hold hope for approaching capacity over fast fading channels. However most of these schemes employ low rate convolutional codes as their channel encoders. In this thesis we propose the use of redundant residue number system codes. It is shown that these codes can achieve comparable performance at minimal complexity and high data rates. The second part deals with the possibility of combining several diversity dimensions into a reliable bandwidth efficient communication scheme. Orthogonal frequency division multiplexing (OFDM) has been used to combat multipaths. Combining OFDM with multiple-input multiple-output (MIMO) systems to form MIMO-OFDM not only reduces the complexity by eliminating the need for equalization but also provides large channel capacity and a high diversity potential. Space-time coded OFDM was proposed and shown to be an effective transmission technique for MIMO systems. Spacefrequency coding and space-time-frequency coding were developed out of the need to exploit the frequency diversity due to multipaths. Most of the proposed schemes in the literature maximize frequency diversity predominantly from the frequency-selective nature of the fading channel. In this thesis we propose the use of residue number system as the frequency encoder. It is shown that the proposed space-time-frequency coding scheme can maximize the diversity gains over space, time and frequency domains. The gain of MIMO-OFDM comes at the expense of increased receiver complexity. Furthermore, most of the proposed space-time-frequency coding schemes assume frequency selective block fading channels which is not an ideal assumption for broadband wireless communications. Relatively high mobility in broadband wireless communications systems may result in high Doppler frequency, hence time-selective (rapid) fading. Rapidly changing channel characteristics impedes the channel estimation process and may result in incorrect estimates of the channel coefficients. The last part of the thesis deals with the performance of differential space-time-frequency coding in fast fading channels

Physical Layer Implementation of a class of ZigBee Baseband Transceiver using FPGA

ZigBee and IEEE 802.15.4 standard for wireless technology, developed in 2003 were designed for interconnection of data communication using low data rate, low power and low complexity short range communication in a wireless personal area network (WPAN). Later in 2006, it was enhanced for market applicability to remove ambiguities in implementation for low data rate and short range wireless networks with high battery life. This technology supports cost effective, low power, wireless network monitoring and control products based on open global standard. This thesis presents a FPGA implementation of Baseband physical layer for ZigBee. It presents the designs, implementation, verification and validation.The ZigBee baseband transceiver proposed in this thesis is based on IEEE 802.15.4 where the transceiver uses OQPSK modulation. DS spread spectrum and half sine pulse shaping is used for coding and baseband processing respectively. The transceiver is initially simulated in matlab software using Simulink and next it was simulated in verilog HDL by the mentor graphics modelsim simulator. Subsequently the baseband transceiver system was realized on Virtex 5 FPGA using ISE design environment. Further a new form of baseband transceiver was designed using PN sequence generated by Residue number system (RNS). The performance of the transceiver using RNS system was first analyzed through matlab simulation. Following this the transceiver was implemented on Virtex 5 FPGA in ISE design environment

Nonlinear Fourier transform for optical data processing and transmission:advances and perspectives

Fiber-optic communication systems are nowadays facing serious challenges due to the fast growing demand on capacity from various new applications and services. It is now well recognized that nonlinear effects limit the spectral efficiency and transmission reach of modern fiber-optic communications. Nonlinearity compensation is therefore widely believed to be of paramount importance for increasing the capacity of future optical networks. Recently, there has been steadily growing interest in the application of a powerful mathematical tool-the nonlinear Fourier transform (NFT)-in the development of fundamentally novel nonlinearity mitigation tools for fiber-optic channels. It has been recognized that, within this paradigm, the nonlinear crosstalk due to the Kerr effect is effectively absent, and fiber nonlinearity due to the Kerr effect can enter as a constructive element rather than a degrading factor. The novelty and the mathematical complexity of the NFT, the versatility of the proposed system designs, and the lack of a unified vision of an optimal NFT-type communication system, however, constitute significant difficulties for communication researchers. In this paper, we therefore survey the existing approaches in a common framework and review the progress in this area with a focus on practical implementation aspects. First, an overview of existing key algorithms for the efficacious computation of the direct and inverse NFT is given, and the issues of accuracy and numerical complexity are elucidated. We then describe different approaches for the utilization of the NFT in practical transmission schemes. After that we discuss the differences, advantages, and challenges of various recently emerged system designs employing the NFT, as well as the spectral efficiency estimates available up-to-date. With many practical implementation aspects still being open, our mini-review is aimed at helping researchers assess the perspectives, understand the bottlenecks, and envision the development paths in the upcoming NFT-based transmission technologies

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications