On distributed coding, quantization of channel measurements and faster-than-Nyquist signaling

This dissertation considers three different aspects of modern digital communication systems and is therefore divided in three parts. The first part is distributed coding. This part deals with source and source- channel code design issues for digital communication systems with many transmitters and one receiver or with one transmitter and one receiver but with side information at the receiver, which is not available at the transmitter. Such problems are attracting attention lately, as they constitute a way of extending the classical point-to-point communication theory to networks. In this first part of this dissertation, novel source and source-channel codes are designed by converting each of the considered distributed coding problems into an equivalent classical channel coding or classical source-channel coding problem. The proposed schemes come very close to the theoretical limits and thus, are able to exhibit some of the gains predicted by network information theory. In the other two parts of this dissertation classical point-to-point digital com- munication systems are considered. The second part is quantization of coded chan- nel measurements at the receiver. Quantization is a way to limit the accuracy of continuous-valued measurements so that they can be processed in the digital domain. Depending on the desired type of processing of the quantized data, different quantizer design criteria should be used. In this second part of this dissertation, the quantized received values from the channel are processed by the receiver, which tries to recover the transmitted information. An exhaustive comparison of several quantization cri- teria for this case are studied providing illuminating insight for this quantizer design problem. The third part of this dissertation is faster-than-Nyquist signaling. The Nyquist rate in classical point-to-point bandwidth-limited digital communication systems is considered as the maximum transmission rate or signaling rate and is equal to twice the bandwidth of the channel. In this last part of the dissertation, we question this Nyquist rate limitation by transmitting at higher signaling rates through the same bandwidth. By mitigating the incurred interference due to the faster-than-Nyquist rates, gains over Nyquist rate systems are obtained

Distributed Joint Source-Channel Coding in Wireless Sensor Networks

Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to practice, of distributed joint source-channel coding over the independent channels, the multiple access channels and the broadcast channels are introduced, respectively. To this end, we also present a practical scheme for compressing multiple correlated sources over the independent channels. The simulation results demonstrate the desired efficiency

Design techniques for graph-based error-correcting codes and their applications

In ShannonÂs seminal paper, ÂA Mathematical Theory of CommunicationÂ, he defined ÂChannel Capacity which predicted the ultimate performance that transmission systems can achieve and suggested that capacity is achievable by error-correcting (channel) coding. The main idea of error-correcting codes is to add redundancy to the information to be transmitted so that the receiver can explore the correlation between transmitted information and redundancy and correct or detect errors caused by channels afterward. The discovery of turbo codes and rediscovery of Low Density Parity Check codes (LDPC) have revived the research in channel coding with novel ideas and techniques on code concatenation, iterative decoding, graph-based construction and design based on density evolution. This dissertation focuses on the design aspect of graph-based channel codes such as LDPC and Irregular Repeat Accumulate (IRA) codes via density evolution, and use the technique (density evolution) to design IRA codes for scalable image/video communication and LDPC codes for distributed source coding, which can be considered as a channel coding problem. The first part of the dissertation includes design and analysis of rate-compatible IRA codes for scalable image transmission systems. This part presents the analysis with density evolution the effect of puncturing applied to IRA codes and the asymptotic analysis of the performance of the systems. In the second part of the dissertation, we consider designing source-optimized IRA codes. The idea is to take advantage of the capability of Unequal Error Protection (UEP) of IRA codes against errors because of their irregularities. In video and image transmission systems, the performance is measured by Peak Signal to Noise Ratio (PSNR). We propose an approach to design IRA codes optimized for such a criterion. In the third part of the dissertation, we investigate Slepian-Wolf coding problem using LDPC codes. The problems to be addressed include coding problem involving multiple sources and non-binary sources, and coding using multi-level codes and nonbinary codes

Layered Wyner-Ziv video coding for noisy channels

The growing popularity of video sensor networks and video celluar phones has generated the need for low-complexity and power-efficient multimedia systems that can handle multiple video input and output streams. While standard video coding techniques fail to satisfy these requirements, distributed source coding is a promising technique for ??uplink?? applications. Wyner-Ziv coding refers to lossy source coding with side information at the decoder. Based on recent theoretical result on successive Wyner-Ziv coding, we propose in this thesis a practical layered Wyner-Ziv video codec using the DCT, nested scalar quantizer, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information) for noiseless channel. The DCT is applied as an approximation to the conditional KLT, which makes the components of the transformed block conditionally independent given the side information. NSQ is a binning scheme that facilitates layered bit-plane coding of the bin indices while reducing the bit rate. LDPC code based Slepian-Wolf coding exploits the correlation between the quantized version of the source and the side information to achieve further compression. Different from previous works, an attractive feature of our proposed system is that video encoding is done only once but decoding allowed at many lower bit rates without quality loss. For Wyner-Ziv coding over discrete noisy channels, we present a Wyner-Ziv video codec using IRA codes for Slepian-Wolf coding based on the idea of two equivalent channels. For video streaming applications where the channel is packet based, we apply unequal error protection scheme to the embedded Wyner-Ziv coded video stream to find the optimal source-channel coding trade-off for a target transmission rate over packet erasure channel

Universal codes in the shared-randomness model for channels with general distortion capabilities

We put forth new models for universal channel coding. Unlike standard codes which are designed for a specific type of channel, our most general universal code makes communication resilient on every channel, provided the noise level is below the tolerated bound, where the noise level t of a channel is the logarithm of its ambiguity (the maximum number of strings that can be distorted into a given one). The other more restricted universal codes still work for large classes of natural channels. In a universal code, encoding is channel-independent, but the decoding function knows the type of channel. We allow the encoding and the decoding functions to share randomness, which is unavailable to the channel. There are two scenarios for the type of attack that a channel can perform. In the oblivious scenario, codewords belong to an additive group and the channel distorts a codeword by adding a vector from a fixed set. The selection is based on the message and the encoding function, but not on the codeword. In the Hamming scenario, the channel knows the codeword and is fully adversarial. For a universal code, there are two parameters of interest: the rate, which is the ratio between the message length k and the codeword length n, and the number of shared random bits. We show the existence in both scenarios of universal codes with rate 1-t/n - o(1), which is optimal modulo the o(1) term. The number of shared random bits is O(log n) in the oblivious scenario, and O(n) in the Hamming scenario, which, for typical values of the noise level, we show to be optimal, modulo the constant hidden in the O() notation. In both scenarios, the universal encoding is done in time polynomial in n, but the channel-dependent decoding procedures are in general not efficient. For some weaker classes of channels we construct universal codes with polynomial-time encoding and decoding.Comment: Removed the mentioning of online matching, which is not used her

Multiterminal source coding: sum-rate loss, code designs, and applications to video sensor networks

Driven by a host of emerging applications (e.g., sensor networks and wireless video), distributed source coding (i.e., Slepian-Wolf coding, Wyner-Ziv coding and various other forms of multiterminal source coding), has recently become a very active research area. This dissertation focuses on multiterminal (MT) source coding problem, and consists of three parts. The first part studies the sum-rate loss of an important special case of quadratic Gaussian multi-terminal source coding, where all sources are positively symmetric and all target distortions are equal. We first give the minimum sum-rate for joint encoding of Gaussian sources in the symmetric case, and then show that the supremum of the sum-rate loss due to distributed encoding in this case is 1 2 log2 5 4 = 0:161 b/s when L = 2 and increases in the order of º L 2 log2 e b/s as the number of terminals L goes to infinity. The supremum sum-rate loss of 0:161 b/s in the symmetric case equals to that in general quadratic Gaussian two-terminal source coding without the symmetric assumption. It is conjectured that this equality holds for any number of terminals. In the second part, we present two practical MT coding schemes under the framework of Slepian-Wolf coded quantization (SWCQ) for both direct and indirect MT problems. The first, asymmetric SWCQ scheme relies on quantization and Wyner-Ziv coding, and it is implemented via source splitting to achieve any point on the sum-rate bound. In the second, conceptually simpler scheme, symmetric SWCQ, the two quantized sources are compressed using symmetric Slepian-Wolf coding via a channel code partitioning technique that is capable of achieving any point on the Slepian-Wolf sum-rate bound. Our practical designs employ trellis-coded quantization and turbo/LDPC codes for both asymmetric and symmetric Slepian-Wolf coding. Simulation results show a gap of only 0.139-0.194 bit per sample away from the sum-rate bound for both direct and indirect MT coding problems. The third part applies the above two MT coding schemes to two practical sources, i.e., stereo video sequences to save the sum rate over independent coding of both sequences. Experiments with both schemes on stereo video sequences using H.264, LDPC codes for Slepian-Wolf coding of the motion vectors, and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients give slightly smaller sum rate than separate H.264 coding of both sequences at the same video quality

Polar codes for distributed source coding

Ankara : The Department of Electrical and Electronics Engineering and The Graduate School of Engineering and Science of Bilkent Univesity, 2014.Thesis (Ph. D.) -- Bilkent University, 2014.Includes bibliographical references leaves 164-170.Polar codes were invented by Arıkan as the first “capacity achieving” codes for binary-input discrete memoryless symmetric channels with low encoding and decoding complexity. The “polarization phenomenon”, which is the underlying principle of polar codes, can be applied to different source and channel coding problems both in single-user and multi-user settings. In this work, polar coding methods for multi-user distributed source coding problems are investigated. First, a restricted version of lossless distributed source coding problem, which is also referred to as the Slepian-Wolf problem, is considered. The restriction is on the distribution of correlated sources. It is shown that if the sources are “binary symmetric” then single-user polar codes can be used to achieve full capacity region without time sharing. Then, a method for two-user polar coding is considered which is used to solve the Slepian-Wolf problem with arbitrary source distributions. This method is also extended to cover multiple-access channel problem which is the dual of Slepian-Wolf problem. Next, two lossy source coding problems in distributed settings are investigated. The first problem is the distributed lossy source coding which is the lossy version of the Slepian-Wolf problem. Although the capacity region of this problem is not known in general, there is a good inner bound called the Berger-Tung inner bound. A polar coding method that can achieve the whole dominant face of the Berger-Tung region is devised. The second problem considered is the multiple description coding problem. The capacity region for this problem is also not known in general. El Gamal-Cover inner bound is the best known bound for this problem. A polar coding method that can achieve any point on the dominant face of El Gamal-Cover region is devised.Önay, SaygunPh.D

Codage de sources avec information adjacente et connaissance incertaine des corrélations

Dans cette thèse, nous nous sommes intéressés au problème de codage de sources avec information adjacente au décodeur seulement. Plus précisément, nous avons considéré le cas où la distribution jointe entre la source et l'information adjacente n'est pas bien connue. Dans ce contexte, pour un problème de codage sans pertes, nous avons d'abord effectué une analyse de performance à l'aide d'outils de la théorie de l'information. Nous avons ensuite proposé un schéma de codage pratique efficace malgré le manque de connaissance sur la distribution de probabilité jointe. Ce schéma de codage s'appuie sur des codes LDPC non-binaires et sur un algorithme de type Espérance-Maximisation. Le problème du schéma de codage proposé, c'est que les codes LDPC non-binaires utilisés doivent être performants. C'est à dire qu'ils doivent être construits à partir de distributions de degrés qui permettent d'atteindre un débit proche des performances théoriques. Nous avons donc proposé une méthode d'optimisation des distributions de degrés des codes LDPC. Enfin, nous nous sommes intéressés à un cas de codage avec pertes. Nous avons supposé que le modèle de corrélation entre la source et l'information adjacente était décrit par un modèle de Markov caché à émissions Gaussiennes. Pour ce modèle, nous avons également effectué une analyse de performance, puis nous avons proposé un schéma de codage pratique. Ce schéma de codage s'appuie sur des codes LDPC non-binaires et sur une reconstruction MMSE. Ces deux composantes exploitent la structure avec mémoire du modèle de Markov caché.In this thesis, we considered the problem of source coding with side information available at the decoder only. More in details, we considered the case where the joint distribution between the source and the side information is not perfectly known. In this context, we performed a performance analysis of the lossless source coding scheme. This performance analysis was realized from information theory tools. Then, we proposed a practical coding scheme able to deal with the uncertainty on the joint probability distribution. This coding scheme is based on non-binary LDPC codes and on an Expectation-Maximization algorithm. For this problem, a key issue is to design efficient LDPC codes. In particular, good code degree distributions have to be selected. Consequently, we proposed an optimization method for the selection of good degree distributions. To finish, we considered a lossy coding scheme. In this case, we assumed that the correlation channel between the source and the side information is described by a Hidden Markov Model with Gaussian emissions. For this model, we performed again some performance analysis and proposed a practical coding scheme. The proposed scheme is based on non-binary LDPC codes and on MMSE reconstruction using an MCMC method. In our solution, these two components are able to exploit the memory induced by the Hidden Markov model.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF