"Experts in information theory have long been interested in the maximal size, A(n, d), of a binary error-correcting code of length n and minimum distance d, The problem of determining A(n, d) involves both the construction of good codes and the search for good upper bounds. For quite some time now, Delsarte's linear programming approach has been the dominant approach to obtaining the strongest general purpose upper bounds on the efficiency of error-correcting codes. From 1973 forward, the linear programming bound found many applications, but there were few significant theoretical advances until Schrijver proposed a new code upper bound via semidefinite programming in 2003. Using the Terwilliger algebra, a recently introduced extension of the Bose-Mesner algebra, Schrijver formulated a new SDP strengthening of the LP approach. In this project we look at the dual solutions of the semidefinite programming bound for binary error-correcting codes. We explore the combinatorial meaning of these variables for small n and d, such as n = 4 and d = 2. To obtain information like this, we wrote a computer program with both Matlab and CVX modules to get solution of our primal SDP formulation. Our program efficiently generates the primal solutions with corresponding constraints for any n and d. We also wrote a program in C++ to parse the output of the primal SDP problem, and another Matlab script to generate the dual SDP problem, which could be used in assigning combinatorial meaning to the values given in the dual optimal solution. Our code not only computes both the primal and dual optimal variable values, but allows the researcher to display them in meaningful ways and to explore their relationship and dependence on arameters. These values are expected to be useful for later study of the combinatorial meaning of such solutions."

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The classical binary error correcting codes, and subspace codes for error correction in random network coding are two different forms of error control coding. We identify common features between these two forms and study the relations between them using the aid of lattices. Lattices are partial ordered sets where every pair of elements has a least upper bound and a greatest lower bound in the lattice. We shall demonstrate that many questions that connect these forms have a natural motivation from the viewpoint of lattices. We shall show that a lattice framework captures the notion of Singleton bound where the bound is on the size of the code as a function of its parameters. For the most part, we consider a special type of a lattice which has the geometric modular property. We will use a lattice framework to combine the two different forms. And then, in order to demonstrate the utility of this binding view, we shall derive a general version of Singleton bound. We will note that the Singleton bounds behave differently in certain respects because the binary coding framework is associated with a lattice that is distributive. We shall demonstrate that lack of distributive gives rise to a weaker bound. We show that Singleton bound for classical binary codes, subspace codes, rank metric codes and Ferrers diagram rank metric codes can be derived using a common technique. In the literature, Singleton bounds are derived for Ferrers diagram rank metric codes where the rank metric codes are linear. We introduce a generalized version of Ferrers diagram rank metric codes and obtain a Singleton bound for this version. Next, we shall prove a conjecture concerning the constraints of embedding a binary coding framework into a subspace framework. We shall prove a conjecture by Braun, Etzion and Vardy, which states that any such embedding which contains the full space in its range is constrained to have a particular size. Our proof will use a theorem due to Lovasz, a subspace counting theorem for geometric modular lattices, to prove the conjecture. We shall further demonstrate that any code that achieves the conjectured size must be of a particular type. This particular type turns out to be a natural distributive sub-lattice of a given geometric modular lattice.

Les systèmes de téléphonie mobile de 4ème et 5ème générations ont adopté comme techniques de codage de canal les turbocodes, les codes LDPC et les codes polaires binaires. Cependant, ces codes ne permettent pas de répondre aux exigences, en termes d’efficacité spectrale et de fiabilité, pour les réseaux de communications futurs (2030 et au-delà), qui devront supporter de nouvelles applications telles que les communications holographiques, les véhicules autonomes, l’internet tactile … Un premier pas a été fait il y a quelques années vers la définition de codes correcteurs d’erreurs plus puissants avec l’étude de codes LDPC non binaires, qui ont montré une meilleure performance que leurs équivalents binaires pour de petites tailles de code et/ou lorsqu'ils sont utilisés sur des canaux non binaires. En contrepartie, les codes LDPC non binaires présentent une complexité de décodage plus importante que leur équivalent binaire. Des études similaires ont commencé à émerger du côté des turbocodes. Tout comme pour leurs homologues LDPC, les turbocodes non binaires présentent d’excellentes performances pour de petites tailles de blocs. Du point de vue du décodage, les turbocodes non binaires sont confrontés au même problème d’augmentation de la complexité de traitement que les codes LDPC non binaire. Dans cette thèse nous avons proposé une nouvelle structure de turbocodes non binaires en optimisant les différents blocs qui la constituent. Nous avons réduit la complexité de ces codes grâce à la définition d’un algorithme de décodage simplifié. Les codes obtenus ont montré des performances intéressantes en comparaison avec les codes correcteur d’erreur de la littérature. / Nowadays communication standards have adopted different binary forward error correction codes. Turbo codes were adopted for the long term evolution standard, while binary LDPC codes were standardized for the fifth generation of mobile communication (5G) along side with the polar codes. Meanwhile, the focus of the communication community is shifted towards the requirement of beyond 5G standards. Networks for the year 2030 and beyond are expected to support novel forward-looking scenarios, such as holographic communications, autonomous vehicles, massive machine-type communications, tactile Internet… To respond to the expected requirements of new communication systems, non-binary LDPC codes were defined, and they are shown to achieve better error correcting performance than the binary LDPC codes. This performance gain was followed by a high decoding complexity, depending on the field order.Similar studies emerged in the context of turbo codes, where the non-binary turbo codes were defined, and have shown promising error correcting performance, while imposing high complexity. The aim of this thesis is to propose a new low-complex structure of non-binary turbocodes. The constituent blocks of this structure were optimized in this work, and a new low complexity decoding algorithm was proposed targeting a future hardware implementation. The obtained results are promising, where the proposed codes are shown to outperform existing binary and non-binary codes from the literature.

One of the facets of the mobile or wireless environment is that errors quite often occur in bursts. Thus, strong codes are required to provide protection against such errors. This in turn motivates the employment of decoding algorithms which are simple to implement, yet are still able to attempt to take the dependence or memory of the channel model into account in order to give optimal decoding estimates. Furthermore, such algorithms should be able to be applied for a variety of channel models and signalling alphabets. The research presented within this thesis describes a number of algorithms which can be used with linear block codes. Given the received word, these algorithms determine the symbol which was most likely transmitted, on a symbol-by-symbol basis. Due to their relative simplicity, a collection of algorithms for memoryless channels is reported first. This is done to establish the general style and principles of the overall collection. The concept of matrix diagonalisation may or may not be applied, resulting in two different types of procedure. Ultimately, it is shown that the choice between them should be motivated by whether storage space or computational complexity has the higher priority. As with all other procedures explained herein, the derivation is first performed for a binary signalling alphabet and then extended to fields of prime order. These procedures form the paradigm for algorithms used in conjunction with finite state channel models, where errors generally occur in bursts. In such cases, the necessary information is stored in matrices rather than as scalars. Finally, by analogy with the weight polynomials of a code and its dual as characterised by the MacWilliams identities, new procedures are developed for particular types of Gilbert-Elliott channel models. Here, the calculations are derived from three parameters which profile the occurrence of errors in those models. The decoding is then carried out using polynomial evaluation rather than matrix multiplication. Complementing this theory are several examples detailing the steps required to perform the decoding, as well as a collection of simulation results demonstrating the practical value of these algorithms.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes. / Graduate Studies, College of (Okanagan) / Graduate