Two broad classes of graphical modeling problems for codes can be identified in the literature: constructive and extractive problems. The former class of problems concern the construction of a graphical model in order to define a new code. The latter class of problems concern the extraction of a graphical model for a (fixed) given code. The design of a new low-density parity-check code for some given criteria (e.g. target block length and code rate) is an example of a constructive problem. The determination of a graphical model for a classical linear block code which implies a decoding algorithm with desired performance and complexity characteristics is an example of an extractive problem. This work focuses on extractive graphical model problems and aims to lay out some of the foundations of the theory of such problems for linear codes. The primary focus of this work is a study of the space of all graphical models for a (fixed) given code. The tradeoff between cyclic topology and complexity in this space is characterized via the introduction of a new bound: the tree-inducing cut-set bound. The proposed bound provides a more precise characterization of this tradeoff than that which can be obtained using existing tools (e.g. the Cut-Set Bound) and can be viewed as a generalization of the square-root bound for tail-biting trellises to graphical models with arbitrary cyclic topologies. Searching the space of graphical models for a given code is then enabled by introducing a set of basic graphical model transformation operations which are shown to span this space. Finally, heuristics for extracting novel graphical models for linear block codes using these transformations are investigated.
Two broad classes of graphical modeling problems for codes can be identified in the literature: constructive and extractive problems. The former class of problems concern the construction of a graphical model in order to define a new code. The latter class of problems concern the extraction of a graphical model for a (fixed) given code. The design of a new low-density parity-check code for some given criteria (e.g. target block length and code rate) is an example of a constructive problem. The determination of a graphical model for a classical linear block code which implies a decoding algorithm with desired performance and complexity characteristics is an example of an extractive problem. This work focuses on extractive graphical model problems and aims to lay out some of the foundations of the theory of such problems for linear codes. The primary focus of this work is a study of the space of all graphical models for a (fixed) given code. The tradeoff between cyclic topology and complexity in this space is characterized via the introduction of a new bound: the tree-inducing cut-set bound. The proposed bound provides a more precise characterization of this tradeoff than that which can be obtained using existing tools (e.g. the Cut-Set Bound) and can be viewed as a generalization of the square-root bound for tail-biting trellises to graphical models with arbitrary cyclic topologies. Searching the space of graphical models for a given code is then enabled by introducing a set of basic graphical model transformation operations which are shown to span this space. Finally, heuristics for extracting novel graphical models for linear block codes using these transformations are investigated.
Two broad classes of graphical modeling problems for codes can be identified in the literature: constructive and extractive problems. The former class of problems concern the construction of a graphical model in order to define a new code. The latter class of problems concern the extraction of a graphical model for a (fixed) given code. The design of a new low-density parity-check code for some given criteria (e.g., target block length and code rate) is an example of a constructive problem. The determination of a graphical model for a classical linear block code that implies a decoding algorithm with desired performance and complexity characteristics is an example of an extractive problem. This work focuses on extractive graphical model problems and aims to lay out some of the foundations of the theory of such problems for linear codes. The primary focus of this work is a study of the space of all graphical models for a (fixed) given code. The tradeoff between cyclic topology and complexity in this space is characterized via the introduction of a new bound: the forest-inducing cut-set bound (FI-CSB). The proposed bound provides a more precise characterization of this tradeoff than that which can be obtained using existing tools (e.g., the CSB) and can be viewed as a generalization of the square-root bound for tail-biting trellises to graphical models with arbitrary cyclic topologies. Searching the space of graphical models for a given code is then enabled by introducing a set of basic graphical model transformation operations that are shown to span this space. Finally, heuristics for extracting novel graphical models for linear block codes using these transformations are investigated.
Since the discovery of turbo codes in the 1990s, much of the work concerning graphical models for codes has focused on constructing models in order to define a new code. This work focuses on the theoretical foundations of the inverse problem to code construction -extracting a graphical model for a given (fixed) code. Specifically, limits on the space of graphical models for a given code are examined. A number of results concerning these limits (e.g., the cut-set bound, the square-root bound) are first reviewed. Following this review, recent results concerning these limits are presented. It is first shown that the Tanner graphs of many classical block codes necessarily contain many short cycles. It is then shown that cycle-free binary graphical models do not support good codes thus generalizing Trachtenberg's work concerning the existence of cycle-free Tanner graphs. Finally, and most significantly, the tree-inducing cut-set bound (TI-CSB) is derived and discussed. The TI-CSB provides a characterization of the tradeoff between complexity and density in the family of graphical models for a given code. For example, the TI-CSB can be used to show that anrth-root complexity reduction (with respect to the least-complex cycle-free model) requires the introduction of at leastr(r- 1)/2 cycles thus generalizing the square-root bound to graphical models with arbitrary cyclic topologies.
It is now well-known that together with a suitable message passing schedule, a graphical model for a code implies a soft-in soft-out (SISO) decoding algorithm which is optimal for cycle-free models and suboptimal, yet often substantially less complex, for cyclic models.The Cut-Set Bound (CSB) indicates that the introduction of cycles to graphical models can lead to dramatic reductions in complexity and in the case of tailbiting trellises (i.e.single-cycle models), the CSB can be used to establish the square-root bound, thus quantifying this potential reduction precisely.The aim of this work is to provide such a precise characterization of the tradeoff between cyclic topology and complexity (i.e.maximum hidden variable size and local constraint complexity) in graphical models with more than one cycle.To this end, a new bound is introduced -the tree-inducing cut-set bound -which can be viewed as a generalization of the square-root bound to graphical models with arbitrary cyclic topologies.
Since the invention of turbo codes, there has been an explosion of interest in iterative decoding and graphical representation of codes. This thesis examines the iterative decoding of codes defined on graphs with cycles, which appears to be an efficient means of achieving the Shannon limit. Much of this analysis is on the iterative min-sum decoding of tail-biting codes and cycle codes. We have identified the pseudocodeword as the cause of the suboptimal performance of the iterative decoder, and we have obtained a union bound for the performance of the iterative decoder on both AWGN and BSC channels. Using the union bound argument, for cycle codes, we have shown that the performance of the iterative decoder is asymptotically as good as that of the ML decoder. As for tail-biting codes, the same thing is true if the lowest weight pseudocodeword is at least the minimum weight of the code. Unfortunately, the analysis of tail-biting codes and cycle codes does not extend to turbo codes and low density parity check codes in general. Our next approach is to determine the average behavior of message passing algorithms by studying the evolution of their "message" densities. For the class of "repeat and accumulate" serially concatenated turbo-like codes, we have devised an algorithm for determining their "threshold" values. When the signal-to-noise ratio is larger than the threshold value, the error probabilities of message passing algorithms approach zero, whereas if the signal-to-noise ratio is less than the threshold value, the error probability stays bounded away from zero. Some message passing algorithms and graphical representation of codes are efficient means of devising ML or MAP decoding algorithms. We have proposed a junction tree representation for linear block codes, and we have shown that the minimum junction tree can be less complex than the minimal trellis.
Since their rediscovery in the late 1990s, low-density parity-check (LDPC) codes have rapidly become one the most important classes of error correcting codes. LDPC codes have a graphical description and are generally decoded iteratively. The graph of an iteratively decoded code's error rate performance versus the channel quality is typically divided into two regions. The error floor region appears at higher channel quality and is characterized by a more gradual decrease in error rate as channel quality improves. For iterative decoders the error floor is apparently determined by small structures within the code that are specific to the selected graphical description of the code and subtle details of the decoding algorithm. This thesis addresses several issues of LDPC error floors in the additive white Gaussian noise channel. First, we find the source of several well publicized error floors to be numerical issues within the message-passing decoder. We propose numerical improvements to several decoding formulations which substantially lower error floors. Next, we examine and refine error-floor prediction models. A time-variant linear state-space model is presented and analyzed. Graph theory is applied to analyze the small error-prone structures within the code. While we find our techniques encounter considerable problems in determining the error floors for iterative decoders with unrestricted dynamic range, their predictions are quite accurate when significant range restrictions are present. The final issue of LDPC error floors which we address relates to the minimum distance of quasi-cyclic (QC) LDPC codes. We derive improved minimum distance upper bounds for codes which are punctured and codes which contain a significant number of zeros in their protomatrices. These bound are then applied to a family of protograph-based LDPC codes standardized for use in deep-space communications. In the case of the rate-1/2 code, our upper bounds demonstrate that the previously promulgated ensemble lower bounds do not apply to QC constructions at block lengths beyond 4400 bits. The last part of this dissertation is a fairly separate piece of work on bounding the spectral radius of a large class of nonnegative matrices. We apply these bounds to digraphs and derive several associated equality conditions.</
Since their rediscovery in the late 1990s, low-density parity-check (LDPC) codes have rapidly become one the most important classes of error correcting codes. LDPC codes have a graphical description and are generally decoded iteratively. The graph of an iteratively decoded code's error rate performance versus the channel quality is typically divided into two regions. The error floor region appears at higher channel quality and is characterized by a more gradual decrease in error rate as channel quality improves. For iterative decoders the error floor is apparently determined by small structures within the code that are specific to the selected graphical description of the code and subtle details of the decoding algorithm. This thesis addresses several issues of LDPC error floors in the additive white Gaussian noise channel. First, we find the source of several well publicized error floors to be numerical issues within the message-passing decoder. We propose numerical improvements to several decoding formulations which substantially lower error floors. Next, we examine and refine error-floor prediction models. A time-variant linear state-space model is presented and analyzed. Graph theory is applied to analyze the small error-prone structures within the code. While we find our techniques encounter considerable problems in determining the error floors for iterative decoders with unrestricted dynamic range, their predictions are quite accurate when significant range restrictions are present. The final issue of LDPC error floors which we address relates to the minimum distance of quasi-cyclic (QC) LDPC codes. We derive improved minimum distance upper bounds for codes which are punctured and codes which contain a significant number of zeros in their protomatrices. These bound are then applied to a family of protograph-based LDPC codes standardized for use in deep-space communications. In the case of the rate-1/2 code, our upper bounds demonstrate that the previously promulgated ensemble lower bounds do not apply to QC constructions at block lengths beyond 4400 bits. The last part of this dissertation is a fairly separate piece of work on bounding the spectral radius of a large class of nonnegative matrices. We apply these bounds to digraphs and derive several associated equality conditions.</