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

This work investigates the possibility to accelerate a procedure in 4G/LTE systems, known as control channel analysis. The aim is to perform the procedure in real-time on cheap and accessible hardware.An LTE decoder implemented in software is modified to perform the procedure.The modified software is analyzed and profiled. The most time-consuming decoding steps are identified and implemented in hardware description language.The results show an acceleration of the most time-consuming steps of almost 50 times faster compared to implementation in software only. Furthermore, the resource utilization of the hardware design scales linearly with respect to faster decode time, if necessary the acceleration can be increased. However, the results from the profiling and time measurements of the software show that the time requirement is violated by other decoding steps.The thesis concludes that an acceleration in hardware of the most time-consuming steps is possible. However, to satisfy the time requirement further decode steps are required to be accelerated and/or a faster processor can be used.

Syftet med den här studien har varit att undersöka fem lärares uppfattningar kring hur de skapar en undervisning som bidrar till att utveckla läsförståelsen hos elever i avkodningssvårigheter. Studiens teoretiska utgångspunkt baseras på den sociokulturella teorin enligt vilken elevernas tillägnande av kunskaper sker inom ramen för den närmaste utvecklingszonen. I studien har en kvalitativ metod använts. Materialinsamlingen genomfördes genom kvalitativa semistrukturerade intervjuer. Materialet analyserades med utgångspunkt i den fenomenografiska ansatsen. Resultatet visar att de huvudsakliga temana i lärares arbetssätt för att utveckla läsförståelse hos elever i avkodningssvårigheter är visuellt stöd, modellerande av läsförståelsestrategier och samtalet som redskap. / The purpose of this study has been to investigate the perceptions of five teachers regarding their teaching of reading comprehension to students with decoding difficulties. The theoretical starting point of the study is based on the sociocultural theory, where the focus is that the students' acquisition of knowledge takes place within the framework of the zone of proximal development. A qualitative method has been used in the study. Through qualitative semi-structured interviews, material collection was carried out. The material was analysed using a phenomenographic approach. The results show that the main themes in teachers' working methods for developing reading comprehension for students in decoding difficulties are visual support, modelling reading comprehension strategies and to use text-talk to reflect and understand the meaning of a text.

Space-Time Block Coding is an efficient communication technique used in multiple-input multiple-output wireless systems. The complexity with which a Space-Time Block Code (STBC) can be decoded is important from an implementation point of view since it directly affects the receiver complexity and speed. In this thesis, we address the problem of designing low complexity decoding techniques for STBCs, and constructing STBCs that achieve high rate and full-diversity with these decoders. This thesis is divided into two parts; the first is concerned with the optimal decoder, viz. the maximum-likelihood (ML) decoder, and the second with non-ML decoders. An STBC is said to be multigroup ML decodable if the information symbols encoded by it can be partitioned into several groups such that each symbol group can be ML decoded independently of the others, and thereby admitting low complexity ML decoding. In this thesis, we first give a new framework for constructing low ML decoding complexity STBCs using codes over the Klein group, and show that almost all known low ML decoding complexity STBCs can be obtained by this method. Using this framework we then construct new full-diversity STBCs that have the least known ML decoding complexity for a large set of choices of number of transmit antennas and rate. We then introduce the notion of Asymptotically-Good (AG) multigroup ML decodable codes, which are families of multigroup ML decodable codes whose rate increases linearly with the number of transmit antennas. We give constructions for full-diversity AG multigroup ML decodable codes for each number of groups g > 1. For g > 2, these are the first instances of g-group ML decodable codes that are AG or have rate more than 1. For g = 2 and identical delay, the new codes match the known families of AG codes in terms of rate. In the final section of the first part we show that the upper triangular matrix R encountered during the sphere-decoding of STBCs can be rank-deficient, thus leading to higher sphere-decoding complexity, even when the rate is less than the minimum of the number of transmit antennas and the number receive antennas. We show that all known AG multigroup ML decodable codes suffer from such rank-deficiency, and we explicitly derive the sphere-decoding complexities of most known AG multigroup ML decodable codes. In the second part of this thesis we first study a low complexity non-ML decoder introduced by Guo and Xia called Partial Interference Cancellation (PIC) decoder. We give a new full-diversity criterion for PIC decoding of STBCs which is equivalent to the criterion of Guo and Xia, and is easier to check. We then show that Distributed STBCs (DSTBCs) used in wireless relay networks can be full-diversity PIC decoded, and we give a full-diversity criterion for the same. We then construct full-diversity PIC decodable STBCs and DSTBCs which give higher rate and better error performance than known multigroup ML decodable codes for similar decoding complexity, and which include other known full-diversity PIC decodable codes as special cases. Finally, inspired by a low complexity essentially-ML decoder given by Sirianunpiboon et al. for the two and three antenna Perfect codes, we introduce a new non-ML decoder called Adaptive Conditional Zero-Forcing (ACZF) decoder which includes the technique of Sirianunpiboon et al. as a special case. We give a full-diversity criterion for ACZF decoding, and show that the Perfect codes for two, three and four antennas, the Threaded Algebraic Space-Time code, and the 4 antenna rate 2 code of Srinath and Rajan satisfy this criterion. Simulation results show that the proposed decoder performs identical to ML decoding for these five codes. These STBCs along with ACZF decoding have the best error performance with least complexity among all known STBCs for four or less transmit antennas.

Dans cette thèse, nous présentons nos travaux dans le domaine des algorithmes de décodage des codes LDPC non-binaires généralisés. Les codes LDPC binaires ont été initialement proposés par Gallager en 1963, et après quelques avancées théoriques fondamentales, ils ont été proposés dans des standards tels que DVB-S2, WI-MAX, DSL, W-LAN etc. Plus tard, les codes LDPC non-binaires (NB-LDPC) ont été pro- posés dans la littérature, et ont montré une meilleure performance pour de petites tailles de code ou lorsqu'ils sont utilisés sur des canaux non-binaires. Cependant, les avan- tages de l'utilisation de codes NB-LDPC impliquent une augmentation importante de la complexité de décodage. Pour un code défini dans un corps de Galois GF (q), la complexité est d'ordre O (q2). De même, la mémoire requise pour le stockage des messages est d'ordre O (q). Ainsi, l'implémentation d'un décodeur LDPC défini sur un corps de Galois pour q > 64 devient impossible dans la pratique. L'objectif prin- cipal de cette thèse est de développer des algorithmes avec une bonne performance et complexité réduite de sorte qu'ils deviennent implémentables. Pour une performance de décodage optimisée, non seulement l'algorithme est important, mais également la structure du code joue un rôle clé. Avec cet objectif à l'esprit, une nouvelle famille de codes appelés " cluster-NB-LDPC codes " a été élaborée ainsi que des améliorations spécifiques du décodeur non-binaire pour ces codes. Le résultat principal est que nous avons pu proposer des décodeurs pour les codes cluster-NB-LDPC avec une complex- ité réduite par rapport aux décodeurs classiques pour les codes NB-LDPC définis sur les corps de Galois, sans aucune perte de performance dans la capacité de correction vi Résumé d'erreur. Dans la première partie de la thèse, nous avons modifié l'algorithme EMS pour les cluster-codes. La généralisation directe de l'algorithme EMS aux codes cluster-NB- LDPC n'est pas réaliste . Il y a une perte de performance et une augmentation de la complexité. Par conséquent, nous proposons quelques modifications dans la procé- dure, qui non seulement améliore considérablement les performances de décodage, mais diminue également la complexité. Au niveau des noeuds de parité, cet algo- rithme conserve les mêmes limites sur le nombre d'opérations que l'algorithme EMS pour GF (q)-codes, O (nmlognm) avec nm << q. Nous proposons ensuite une autre méthode, basée sur la diversité des codes cluster, afin d'améliorer les performances de l'algorithme EMS pour les codes cluster-LDPC. Il contribue également à réduire la complexité globale du décodeur. Finalement, nous comparons les performances de décodage en utilisant cette méthode et analysons l'effet sur la complexité de décodage. Dans la dernière partie du chapitre, nous proposons une nouvelle direction pour le décodage des codes LDPC. Elle est basée sur la création des listes des mots de code qui correspondent à des noeuds de parité. Les listes sont construite de manière récur- sive dans une structure en arbre, ce qui en fait un bon candidat pour l'implémentation matérielle. Il s'agit d'une méthode nouvelle et doit encore être améliorée mais à pre- miére vue nous avons obtenu de bons résultats avec un nombre réduit d'operations.

In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol. In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values. In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network.

In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol. In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values. In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network.

In the development of digital communication and information theory, every channel decoding rule has resulted in a revolution at the time when it was invented. In the area of information theory, early channel coding theorems were established mainly by maximum likelihood decoding, while the arrival of typical sequence decoding signaled the era of multi-user information theory, in which achievability proof became simple and intuitive. Practical channel code design, on the other hand, was based on minimum distance decoding at the early stage. The invention of belief propagation decoding with soft input and soft output, leading to the birth of turbo codes and low-density-parity check (LDPC) codes which are indispensable coding techniques in current communication systems, changed the whole research area so dramatically that people started to use the term "modern coding theory'' to refer to the research based on this decoding rule. In this thesis, we propose a new decoding rule, dubbed jar decoding, which would be expected to bring some new thoughts to both the code performance analysis and the code design. Given any channel with input alphabet X and output alphabet Y, jar decoding rule can be simply expressed as follows: upon receiving the channel output y^n ∈ Y^n, the decoder first forms a set (called a jar) of sequences x^n ∈ X^n considered to be close to y^n and pick any codeword (if any) inside this jar as the decoding output. The way how the decoder forms the jar is defined independently with the actual channel code and even the channel statistics in certain cases. Under this jar decoding, various coding theorems are proved in this thesis. First of all, focusing on the word error probability, jar decoding is shown to be near optimal by the achievabilities proved via jar decoding and the converses proved via a proof technique, dubbed the outer mirror image of jar, which is also quite related to jar decoding. Then a Taylor-type expansion of optimal channel coding rate with finite block length is discovered by combining those achievability and converse theorems, and it is demonstrated that jar decoding is optimal up to the second order in this Taylor-type expansion. Flexibility of jar decoding is then illustrated by proving LDPC coding theorems via jar decoding, where the bit error probability is concerned. And finally, we consider a coding scenario, called interactive encoding and decoding, and show that jar decoding can be also used to prove coding theorems and guide the code design in the scenario of two-way communication.

This thesis investigates a graph and information theoretic approach to design and analysis of low-density parity-check (LDPC) codes and wireless networks. In this work, both LDPC codes and wireless networks are considered as random graphs. This work proposes solutions to important theoretic and practical open problems in LDPC coding, and for the first time introduces a framework for analysis of finite wireless networks. LDPC codes are considered to be one of the best classes of error-correcting codes. In this thesis, several problems in this area are studied. First, an improved decoding algorithm for LDPC codes is introduced. Compared to the standard iterative decoding, the proposed decoding algorithm can result in several orders of magnitude lower bit error rates, while having almost the same complexity. Second, this work presents a variety of bounds on the achievable performance of different LDPC coding scenarios. Third, it studies rate-compatible LDPC codes and provides fundamental properties of these codes. It also shows guidelines for optimal design of rate-compatible codes. Finally, it studies non-uniform and unequal error protection using LDPC codes and explores their applications to data storage systems and communication networks. It presents a new error-control scheme for volume holographic memory (VHM) systems and shows that the new method can increase the storage capacity by more than fifty percent compared to previous schemes. This work also investigates the application of random graphs to the design and analysis of wireless ad hoc and sensor networks. It introduces a framework for analysis of finite wireless networks. Such framework was lacking from the literature. Using the framework, different network properties such as capacity, connectivity, coverage, and routing and security algorithms are studied. Finally, connectivity properties of large-scale sensor networks are investigated. It is shown how unreliability of sensors, link failures, and non-uniform distribution of nodes affect the connectivity of sensor networks.

This thesis investigates a graph and information theoretic approach to design and analysis of low-density parity-check (LDPC) codes and wireless networks. In this work, both LDPC codes and wireless networks are considered as random graphs. This work proposes solutions to important theoretic and practical open problems in LDPC coding, and for the first time introduces a framework for analysis of finite wireless networks. LDPC codes are considered to be one of the best classes of error-correcting codes. In this thesis, several problems in this area are studied. First, an improved decoding algorithm for LDPC codes is introduced. Compared to the standard iterative decoding, the proposed decoding algorithm can result in several orders of magnitude lower bit error rates, while having almost the same complexity. Second, this work presents a variety of bounds on the achievable performance of different LDPC coding scenarios. Third, it studies rate-compatible LDPC codes and provides fundamental properties of these codes. It also shows guidelines for optimal design of rate-compatible codes. Finally, it studies non-uniform and unequal error protection using LDPC codes and explores their applications to data storage systems and communication networks. It presents a new error-control scheme for volume holographic memory (VHM) systems and shows that the new method can increase the storage capacity by more than fifty percent compared to previous schemes. This work also investigates the application of random graphs to the design and analysis of wireless ad hoc and sensor networks. It introduces a framework for analysis of finite wireless networks. Such framework was lacking from the literature. Using the framework, different network properties such as capacity, connectivity, coverage, and routing and security algorithms are studied. Finally, connectivity properties of large-scale sensor networks are investigated. It is shown how unreliability of sensors, link failures, and non-uniform distribution of nodes affect the connectivity of sensor networks.