Robust Lossy Source Coding for Correlated Fading Channels
Robust Lossy Source Coding for Correlated Fading Channels

Author: Shervin Shahidi

Publisher: LAP Lambert Academic Publishing

Published: 2012-08

Total Pages: 124

ISBN-13: 9783659192005

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The joint source-channel coding problem for soft-decision demodulated time-correlated fading channels is investigated without the use channel coding and interleaving. Two robust lossy source coding schemes with low-encoding delay are next proposed for the NBNDC-QB. The first scheme consists of a scalar quantizer, a proper index assignment, and a sequence MAP decoder designed to harness the redundancy left in the quantizer's indices, the channel's soft-decision output and noise correlation. The second scheme is the classical noise resilient vector quantizer known as the channel optimized vector quantizer. It is demonstrated that both systems can successfully exploit the channel's memory and soft-decision information. For the purpose of system design, the recently introduced non-binary noise discrete channel with queue based noise (NBNDC-QB) is adopted. Optimal sequence maximum a posteriori (MAP) detection of a discrete Markov source sent over the NBNDC-QB is first studied. When the Markov source is binary and symmetric, a necessary and sufficient condition under which the MAP decoder is reduced to a simple instantaneous symbol-by-symbol decoder is established.

Robust Lossy Source Coding for Correlated Fading Channels
Robust Lossy Source Coding for Correlated Fading Channels

Author: Shervin Shahidi

Publisher:

Published: 2011

Total Pages: 198

ISBN-13: 9780494771488

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Most of the conventional communication systems use channel interleaving as well as hard decision decoding in their designs, which lead to discarding channel memory and soft-decision information. This simplification is usually done since the complexity of handling the memory or soft-decision information is rather high. In this work, we design two lossy joint source-channel coding (JSCC) schemes that do not use explicit algebraic channel coding for a recently introduced channel model, in order to take advantage of both channel memory and soft-decision information. The channel model, called the non-binary noise discrete channel with queue based noise (NBNDC-QB), obtains closed form expressions for the channel transition distribution, correlation coefficient, and many other channel properties. The channel has binary input and $2^q$-ary output and the noise is a $2^q$-ary Markovian stationary ergodic process, based on a finite queue, where $q$ is the output's soft-decision resolution. We also numerically show that the NBNDC-QB model can effectively approximate correlated Rayleigh fading channels without losing its analytical tractability. The first JSCC scheme is the so called channel optimized vector quantizer (COVQ) and the second scheme consists of a scalar quantizer, a proper index assignment, and a sequence maximum a posteriori (MAP) decoder, designed to harness the redundancy left in the quantizer's indices, the channel's soft-decision output, and noise time correlation. We also find necessary and sufficient condition when the sequence MAP decoder is reduced to an instantaneous symbol-by-symbol decoder, i.e., a simple instantaneous mapping.

An Introduction to Single-User Information Theory
An Introduction to Single-User Information Theory

Author: Fady Alajaji

Publisher: Springer

Published: 2018-04-24

Total Pages: 333

ISBN-13: 9811080011

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This book presents a succinct and mathematically rigorous treatment of the main pillars of Shannon’s information theory, discussing the fundamental concepts and indispensable results of Shannon’s mathematical theory of communications. It includes five meticulously written core chapters (with accompanying problems), emphasizing the key topics of information measures; lossless and lossy data compression; channel coding; and joint source-channel coding for single-user (point-to-point) communications systems. It also features two appendices covering necessary background material in real analysis and in probability theory and stochastic processes. The book is ideal for a one-semester foundational course on information theory for senior undergraduate and entry-level graduate students in mathematics, statistics, engineering, and computing and information sciences. A comprehensive instructor’s solutions manual is available.

MAP Decoding of Correlated Sources
MAP Decoding of Correlated Sources

Author: Seyed Parsa Beheshti

Publisher: LAP Lambert Academic Publishing

Published: 2014-11-24

Total Pages: 184

ISBN-13: 9783659637438

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We consider the joint source-channel coding (JSCC) problem where the real valued outputs of two correlated memoryless Gaussian sources are scalar quantized, bit assigned, and transmitted, without applying any error correcting code, over a multiple access channel (MAC) which consists of two orthogonal point-to-point time-correlated Rayleigh fading sub-channels with soft-decision demodulation. At the receiver side, a joint sequence maximum a posteriori (MAP) detector is used to exploit the correlation between the two sources as well as the redundancy left in the quantizers' indices, the channel's soft-decision outputs, and noise memory. The MAC's sub-channels are modeled via non-binary Markov noise discrete channels recently shown to effectively represent point-to-point fading channels. Two scenarios are studied in this book. In the first scenario, the sources are memoryless and generated according to a bivariate Gaussian distribution with a given correlation parameter. In the second scenario, the sources have memory, captured by a changing correlation parameter which is governed by a two state first order Markov process.

Source-channel Coding for Robust Image Transmission and for Dirty-paper Coding
Source-channel Coding for Robust Image Transmission and for Dirty-paper Coding

Author: Yong Sun

Publisher:

Published: 2007

Total Pages:

ISBN-13:

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In this dissertation, we studied two seemingly uncorrelated, but conceptually related problems in terms of source-channel coding: 1) wireless image transmissionand 2) Costa ("dirty-paper") code design. In the first part of the dissertation, we consider progressive image transmission over a wireless system employing space-time coded OFDM. The space-time coded OFDM system based on a newly built broadband MIMO fading model is theoretically evaluated by assuming perfect channel state information (CSI) at the receiver for coherent detection. Then an adaptive modulation scheme is proposed to pick theconstellation size that offers the best reconstructed image quality for each averagesignal-to-noise ratio (SNR).A more practical scenario is also considered without the assumption of perfect CSI. We employ low-complexity decision-feedback decoding for differentially space-time coded OFDM systems to exploit transmitter diversity. For JSCC, we adopt a product channel code structure that is proven to provide powerful error protection and bursty error correction. To further improve the system performance, we also apply the powerful iterative (turbo) coding techniques and propose the iterative decoding of differentially space-time coded multiple descriptions of images. The second part of the dissertation deals with practical dirty-paper code designs. We first invoke an information-theoretical interpretation of algebraic binning and motivate the code design guidelines in terms of source-channel coding. Then two dirty-paper code designs are proposed. The first is a nested turbo construction based on soft-output trellis-coded quantization (SOTCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. A novel procedure is devised to balance the dimensionalities of the equivalent lattice codes corresponding to SOTCQ and TTCM. The second dirty-paper code design employs TCQ and IRA codes fornear-capacity performance. This is done by synergistically combining TCQ with IRA codes so that they work together as well as they do individually. Our TCQ/IRA design approaches the dirty-paper capacity limit at the low rate regime (e.g., 1:0 bit/sample), while our nested SOTCQ/TTCM scheme provides the best performs so far at medium-to-high rates (e.g.,= 1:0 bit/sample). Thus the two proposed practical code designs are complementary to each other.

MAP Decoding of Correlated Sources Over Soft-Decision Orthogonal Multiple Access Fading Channels with Memory
MAP Decoding of Correlated Sources Over Soft-Decision Orthogonal Multiple Access Fading Channels with Memory

Author:

Publisher:

Published: 2014

Total Pages: 26

ISBN-13:

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We consider the joint source-channel coding (JSCC) problem where the real valued outputs of two correlated memoryless Gaussian sources are scalar quantized, bit assigned, and transmitted, without applying any error correcting code, over a multiple access channel (MAC) which consists of two orthogonal point-to-point time-correlated Rayleigh fading sub-channels with soft-decision demodulation. At the receiver side, a joint sequence maximum a posteriori (MAP) detector is used to exploit the correlation between the two sources as well as the redundancy left in the quantizers' indices, the channel's soft-decision outputs, and noise memory. The MAC's sub-channels are modeled via non-binary Markov noise discrete channels recently shown to effectively represent point-to-point fading channels. Two scenarios are studied. In the first scenario, the sources are memoryless and generated according to a bivariate Gaussian distribution with a given correlation parameter. In the second scenario, the sources have memory captured by a changing correlation parameter governed by a two state first order Markov process. In each scenario, for the simple case of quantizing the sources with two levels, we establish a necessary and a sufficient condition under which the joint sequence MAP decoder can be reduced to a simple instantaneous symbol-by-symbol decoder. Then, using numerical results obtained by system simulation, the theorems are illustrated and it is also verified that JSCC can harness the correlation between sources, redundancies in the source symbols, and statistics of the channel noise to achieve improved signal-to-distortion ratio (SDR) performance. For example, when the memoryless sources are highly correlated and soft-decision quantization is used, JSCC can profit from high correlation in the channel noise process and provide significant SDR gains of up to 6.3 dB over a fully interleaved channel.