Joint source-channel coding (JSCC) has emerged to be a major field of research recently. Channel optimized vector quantization (COVQ) is a simple feasible JSCC scheme introduced for communication over practical channels. In this work, we propose an iterative design algorithm, referred to as the iterative maximum a posteriori (MAP) decoded (IMD) algorithm, to improve COVQ systems. Based on this algorithm, we design a COVQ based on symbol MAP hard-decision demodulation that exploits the non-uniformity of the quantization indices probability distribution. The IMD design algorithm consists of a loop which starts by designing a COVQ, obtaining the index source distribution, updating the discrete memoryless channel (DMC) according to the achieved index distribution, and redesigning the COVQ. This loop stops when the point-to-point distortion is minimized. We consider memoryless Gaussian and Gauss-Markov sources transmitted over binary phase-shift keying modulated additive white Gaussian noise (AWGN) and Rayleigh fading channels. Our scheme, which is shown to have less encoding complexity than conventional COVQ and less encoding complexity and storage requirements than soft-decision demodulated (SDD) COVQ systems, is also shown to provide a notable signal-to-distortion ratio (SDR) gain over the conventional COVQ designed for hard-decision demodulated channels while sometimes matching or exceeding the SDD COVQ performance, especially for higher quantization dimensions and/or rates. In addition to our main result, we also propose another iterative algorithm to design SDD COVQ based on the notion of the JSCC error exponent. This system is shown to have some gain over classical SDD COVQ both in terms of the SDR and the exponent itself. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2008-08-29 17:58:52.329

This thesis presents a clockless stochastic low-density parity-check (LDPC) decoder implemented on a Field-Programmable Gate Array (FPGA). Stochastic computing reduces the wiring complexity necessary for decoding by replacing operations such as multiplication and division with simple logic gates. Clockless decoding increases the throughput of the decoder by eliminating the requirement for node signals to be synchronized after each decoding cycle. With this partial-update algorithm the decoder’s speed is limited by the average wire delay of the interleaver rather than the worst-case delay. This type of decoder has been simulated in the past but not implemented on silicon. The design is implemented on an ALTERA Stratix IV EP4SGX230 FPGA and the frame error rate (FER) performance, throughput and power consumption are presented for (96,48) and (204,102) decoders.

Abstract The problem of receiving direct sequence (DS) spread spectrum, binary phase shift keyed (BPSK) information in pulsed noise and continuous wave (CW) jamming is studied in additive white noise. An automatic gain control is not modelled. The general system theory of receiver analysis is first presented and previous literature is reviewed. The study treats the problem of decision making after matched filter or integrate and dump demodulation. The decision methods have a great effect on system performance with pulsed jamming. The following receivers are compared: hard, soft, quantized soft, signal level based erasure, and chip combiner receivers. The analysis is done using a channel parameter D, and bit error upper bound. Simulations were done in original papers using a convolutionally coded DS/BPSK system. The simulations confirm that analytical results are valid. Final conclusions are based on analytical results. The analysis is done using a Chernoff upper bound and a union bound. The analysis is presented with pulsed noise and CW jamming. The same kinds of methods can also be used to analyse other jamming signals. The receivers are compared under pulsed noise and CW jamming along with white gaussian noise. The results show that noise jamming is more harmful than CW jamming and that a jammer should use a high pulse duty factor. If the jammer cannot optimise a pulse duty factor, a good robust choice is to use continuous time jamming. The best performance was achieved by the use of the chip combiner receiver. Just slightly worse was the quantized soft and signal level based erasure receivers. The hard decision receiver was clearly worse. The soft decision receiver without jammer state information was shown to be the most vulnerable to pulsed jamming. The chip combiner receiver is 3 dB worse than an optimum receiver (the soft decision receiver with perfect channel state information). If a simple implementation is required, the hard decision receiver should be used. If moderate complex implementation is allowed, the quantized soft decision receiver should be used. The signal level based erasure receiver does not give any remarkable improvement, so that it is not worth using, because it is more complex to implement. If receiver complexity is not limiting factor, the chip combiner receiver should be used. Uncoded DS/BPSK systems are vulnerable to jamming and a channel coding is an essential part of antijam communication system. Detecting the jamming and erasing jammed symbols in a channel decoder can remove the effect of pulsed jamming. The realization of erasure receivers is rather easy using current integrated circuit technology.

Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or more Markov processes; the output of one or more of the Markov processes is observed through noise; based on the observations, the original data set is estimated. This dissertation specifically describes the process of encoding and decoding turbo codes. In addition, a more general discussion of iterative decoding is presented. Then, several new applications of iterative decoding are proposed and investigated through computer simulation. The new applications solve two categories of problems: the detection of turbo codes over time-varying channels, and the distributed detection of coded multiple-access signals. Because turbo codes operate at low signal-to-noise ratios, the process of phase estimation and tracking becomes difficult to perform. Additionally, the turbo decoding algorithm requires precise estimates of the channel gain and noise variance. The first significant contribution of this dissertation is a study of several methods of channel estimation suitable specifically for turbo coded systems. The second significant contribution of this dissertation is a proposed method for jointly detecting coded multiple-access signals using observations from several locations, such as spatially separated base stations. The proposed system architecture draws from the concepts of macrodiversity combining, multiuser detection, and iterative decoding. Simulation results show that when the system is applied to the time division multiple-access cellular uplink, a significant improvement in system capacity results. / Ph. D.

The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source-channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis four particular areas in joint source-channel coding are studied: analog (i.e. continuous) bandwidth expansion, distributed source coding over noisy channels, multiple description coding (MDC) and soft decoding. A general analog bandwidth expansion code based on orthogonal polynomials is proposed and analyzed. The code has a performance comparable with other existing schemes. However, the code is more general in the sense that it is implementable for a larger number of source distributions. The problem of distributed source coding over noisy channels is studied. Two schemes are proposed and analyzed for this problem which both work on a sample by sample basis. The first code is based on scalar quantization optimized for a certain channel characteristics. The second code is nonlinear and analog. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al. is studied. A modi fication of the quantization in this method is proposed which yields a performance gain. A well known result in joint source-channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied. / QC 20100920

<p>The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source–channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis two particular areas in joint source–channel coding are studied: multiple description coding (MDC) and soft decoding. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al is studied. A modification of the quantization in this method is proposed which yields a performance gain. A well known result in joint source–channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied.</p>

Linear programming (LP) decoding of low-density parity-check codes was introduced by Feldman et al. in [1]. In his formulation it is assumed that communication takes place over a memoryless channel and that the source is uniform. Here, we extend the LP decoding paradigm by studying its application to scenarios with source non-uniformity and to decoding over channels with memory. We develop two decoders for the scenario of non-uniform memoryless sources transmitted over memoryless channels. The first decoder uses a modified linear cost function which incorporates the a-priori source information and works with systematic codes. The second decoder differs by using non-systematic codes obtained by puncturing lower rate systematic codes and using an “extended decoding polytope.” Simulations show that the modified decoders yield gains over the standard LP decoder. Next, LP decoding is considered for two channels with memory: the binary additive Markov noise channel and the infinite-memory non-ergodic Polya-contagion channel. For the Markov channel, no linear cost function corresponding to maximum likelihood (ML) decoding could be obtained and hence it is unclear how to proceed. For the Polya channel, two LP-based decoders are developed. The first is derived in a straightforward manner from the ML decoding rule of [2]. The second decoder relies on a simplification of the same ML decoding rule which holds for codes containing the all-ones codeword. Simulations are performed for both decoders with regular and irregular LDPC codes and demonstrate relatively good performance with respect to the channel epsilon-capacity. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2008-12-08 16:24:43.358

The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source–channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis two particular areas in joint source–channel coding are studied: multiple description coding (MDC) and soft decoding. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al is studied. A modification of the quantization in this method is proposed which yields a performance gain. A well known result in joint source–channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied. / QC 20101124

<p>Networked embedded control systems are present almost everywhere. A recent trend is to introduce wireless sensor networks in these systems, to take advantage of the added mobility and flexibility offered by wireless solutions. In such networks, the sensor observations are typically quantized and transmitted over noisy links. Concerning the problem of closed-loop control over such non-ideal communication channels, relatively few works have appeared so far. This thesis contributes to this field, by studying some fundamentally important problems in the design of joint source--channel coding and optimal control.</p><p>The main part of the thesis is devoted to joint design of the coding and control for scalar linear plants, whose state feedbacks are transmitted over binary symmetric channels. The performance is measured by a finite-horizon linear quadratic cost function. The certainty equivalence property of the studied systems is utilized, since it simplifies the overall design by separating the estimation and the control problems. An iterative optimization algorithm for training the encoder--decoder pairs, taking channel errors into account in the quantizer design, is proposed. Monte Carlo simulations demonstrate promising improvements in performance compared to traditional approaches.</p><p>Event-triggered control strategies are a promising solution to the problem of efficient utilization of communication resources. The basic idea is to let each control loop communicate only when necessary. Event-triggered and quantized control are combined for plants affected by rarely occurring disturbances. Numerical experiments show that it is possible to achieve good control performance with limited control actuation and sensor communication.</p>

<p>This thesis treats the problem of source coding in situations where the encoded data is subject to errors. The typical scenario is a communication system, where source data such as speech or images should be transmitted from one point to another. A problem is that most communication systems introduce some sort of error in the transmission. A wireless communication link is prone to introduce individual bit errors, while in a packet based network, such as the Internet, packet losses are the main source of error.</p><p>The traditional approach to this problem is to add error correcting codes on top of the encoded source data, or to employ some scheme for retransmission of lost or corrupted data. The source coding problem is then treated under the assumption that all data that is transmitted from the source encoder reaches the source decoder on the receiving end without any errors. This thesis takes another approach to the problem and treats source and channel coding jointly under the assumption that there is some knowledge about the channel that will be used for transmission. Such joint source--channel coding schemes have potential benefits over the traditional separated approach. More specifically, joint source--channel coding can typically achieve better performance using shorter codes than the separated approach. This is useful in scenarios with constraints on the delay of the system.</p><p>Two different flavors of joint source--channel coding are treated in this thesis; multiple description coding and channel optimized vector quantization. Channel optimized vector quantization is a technique to directly incorporate knowledge about the channel into the source coder. This thesis contributes to the field by using channel optimized vector quantization in a couple of new scenarios. Multiple description coding is the concept of encoding a source using several different descriptions in order to provide robustness in systems with losses in the transmission. One contribution of this thesis is an improvement to an existing multiple description coding scheme and another contribution is to put multiple description coding in the context of channel optimized vector quantization. The thesis also presents a simple image coder which is used to evaluate some of the results on channel optimized vector quantization.</p>