No description available.

Low-density parity check (LDPC) codes have experienced tremendous popularity due to their capacity-achieving performance. In this dissertation, several different aspects of LDPC coding and its applications to magnetic storage are investigated. One of the most significant issues that impedes the use of LDPC codes in many systems is the error-rate floor phenomenon associated with their iterative decoders. By delineating the fundamental principles, we extend to partial response channels algorithms for predicting the error rate performance in the floor region for the binary-input AWGN channel. We develop three classes of decoding algorithms for mitigating the error floor by directly tackling the cause of the problem: trapping sets. In our experiments, these algorithms provide multiple orders of improvement over conventional decoders at the cost of various implementation complexity increases.Product codes are widely used in magnetic recording systems where errors are both isolated and bursty. A dual-mode decoding technique for Reed-Solomon-code-based product codes is proposed, where the second decoding mode involves maximum-likelihood erasure decoding of the binary images of the Reed-Solomon codewords. By exploring a tape storage application, we demonstrate that this dual-mode decoding system dramatically improves the performance of product codes. Moreover, the complexity added by the second decoding mode is manageable. We also show the performance of this technique on a product code which has an LDPC code in the columns.Run-length-limited (RLL) codes are ubiquitous in today's disk drives. Using RLL codes has enabled drive designers to pack data very efficiently onto the platter surface by ensuring stable symbol-timing recovery. We consider a concatenation system design with an LDPC code and an RLL code as components to simultaneously achieve desirable features such as: soft information availability to the LDPC decoder, the preservation of the LDPC code's structure, and the capability of correcting long erasure bursts.We analyze the performance of LDPC-coded magnetic recording channel in the presence of media noise. We employ advanced signal processing for the pattern-dependent-noise-predictive channel detectors, and demonstrate that a gain of over 1 dB or a linear density gain of about 8% relative to a comparable Reed-Solomon is attainable by using an LDPC code.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The turbo code is a block code even though a convolutional encoder is used to construct codewords. Its performance depends on the code word length. Since the invention of the turbo code in 1993, most of the bit error rate (BER) evaluations have been performed using large block sizes, i.e., sizes greater than 1000, or even 10,000. However, for telemetry and command, a relatively short message (<500 bits) may be used. This paper investigates the turbo-coded BER performance for short packets. Fading channel is also considered. In addition, biased channel side information is adopted to improve the performance.

Σε διαγράμματα BER, με τα οποία αξιολογείται ένα σύστημα αποκωδικοποίησης, και σε χαμηλά επίπεδα θορύβου, παρατηρείται πολλές φορές η περιοχή Error Floor, όπου η απόδοση του αποκωδικοποιητή δε βελτιώνεται πλέον, καθώς μειώνεται ο θόρυβος. Με πραγματοποίηση εξομοίωσης σε software, το Error Floor συνήθως δεν είναι ορατό, κι έτσι κύριο ζητούμενο είναι η πρόβλεψη της συμπεριφοράς του αποκωδικοποιητή, αλλά και γενικότερα η βελτιστοποίηση της απόδοσής του σε αυτήν την περιοχή. Στην παρούσα διπλωματική εργασία μελετάται η ανεπιτυχής αποκωδικοποίηση ορισμένων κωδικών λέξεων καθώς και ο μηχανισμός ενεργοποίησης των Trapping Sets, δηλαδή δομών, οι οποίες φαίνεται να είναι το κύριο αίτιο εμφάνισης του Error Floor. Xρησιμοποιείται το AWGN μοντέλο καναλιού και κώδικας με αραιό πίνακα ελέγχου ισοτιμίας (LDPC), ενώ οι εξομοιώσεις επαναληπτικών αποκωδικοποιήσεων πραγματοποιούνται σε επίπεδα (Layers), με αλγορίθμους ανταλλαγής μηνυμάτων (Message Passing). Αναλύονται προτεινόμενοι τροποποιημένοι αλγόριθμοι και μελετώνται οι επιπτώσεις του κβαντισμού των δεδομένων. Τέλος, προσδιορίζεται η επίδραση του θορύβου στην αποκωδικοποίηση και αναπτύσσεται ένα ημιαναλυτικό μοντέλο υπολογισμού της πιθανότητας ενεργοποίησης ενός Trapping Set και της πιθανότητας εμφάνισης σφάλματος κατά τη μετάδοση. / In BER plots, which are used in order to evaluate a decoding system, and at low-noise level, the Error Floor region is sometimes observed, where the decoder performance is no longer improved, as noise is reduced. When a simulation is executed using software, the Error Floor region is usually not visible, so the main goal is the prediction of the decoder's behavior, as well as the improvement in general of its performance in that particular region. In this thesis, we study the conditions which result in a decoding failure for specific codewords and a Trapping Set activation. Trapping Sets are structures in a code, which seem to be the main cause of the Error Floor presence in BER plots. For the purpose of our study, we use the AWGN channel model and a linear block code with low density parity check matrix (LDPC), while iterative decoding simulations are executed by splitting the parity check matrix into layers (Layered Decoding) and by using Message Passing algorithms. We propose and analyze three new modified algorithms and we study the effects caused by data quantization. Finally, we determine the noise effects on the decoding procedure and we develop a semi-analytical model used for calculating the probability of a Trapping Set activation and for calculating the error probability during transmission.

Among the recent advances in the area of low-density parity-check (LDPC) codes, protograph-based LDPC codes have the advantages of a simple design procedure and highly structured encoders and decoders. These advantages can also be exploited in the design of protograph-based generalized LDPC (G-LDPC) codes. In this dissertation we provide analytical tools which aid the design of protograph-based LDPC and G-LDPC codes. Specifically, we propose a method for computing the codeword-weight enumerators for finite-length protograph-based G-LDPC code ensembles, and then we consider the asymptotic case when the block-length goes to infinity. These results help the designer identify good ensembles of protograph-based G-LDPC codes in the minimum distance sense (i.e., ensembles which have minimum distances grow linearly with code length). Furthermore, good code ensembles can be characterized by good stopping set, trapping set, or pseudocodeword properties, which assist in the design of G-LDPC codes with low floors. We leverage our method for computing codeword-weight enumerators to compute stopping-set, and pseudocodeword enumerators for the finite-length and the asymptotic ensembles of protograph-based G-LDPC codes. Moreover, we introduce a method for computing trapping set enumerators for finite-length (and asymptotic) protograph-based LDPC code ensembles. Trapping set enumerators for G-LDPC codes represents a more complex problem which we do not consider here. Inspired by our method for computing trapping set enumerators for protograph-based LDPC code ensembles, we developed an algorithm for estimating the trapping set enumerators for a specific LDPC code given its parity-check matrix. We used this algorithm to enumerate trapping sets for several LDPC codes from communication standards. Finally, we study coded-modulation schemes with LDPC codes and pulse position modulation (LDPC-PPM) over the free-space optical channel. We present three different decoding schemes and compare their performances. In addition, we developed a new density evolution tool for use in the design of LDPC codes with good performances over this channel.

Ever since the publication of Shannon's seminal work in 1948, the search for capacity achieving codes has led to many interesting discoveries in channel coding theory. Low-density parity-check (LDPC) codes originally proposed in 1963 were largely forgotten and rediscovered recently. The significance of LDPC codes lies in their capacity approaching performance even when decoded using low complexity sub-optimal decoding algorithms. Iterative decoders are one such class of decoders that work on a graphical representation of a code known as the Tanner graph. Their properties have been well understood in the asymptotic limit of the code length going to infinity. However, the behavior of various decoders for a given finite length code remains largely unknown.An understanding of the failures of the decoders is vital for the error floor analysis of a given code. Broadly speaking, error floor is the abrupt degradation in the frame error rate (FER) performance of a code in the high signal-to-noise ratio domain. Since the error floor phenomenon manifests in the regions not reachable by Monte-Carlo simulations, analytical methods are necessary for characterizing the decoding failures. In this work, we consider hard decision decoders for transmission over the binary symmetric channel (BSC).For column-weight-three codes, we provide tight upper and lower bounds on the guaranteed error correction capability of a code under the Gallager A algorithm by studying combinatorial objects known as trapping sets. For higher column weight codes, we establish bounds on the minimum number of variable nodes that achieve certain expansion as a function of the girth of the underlying Tanner graph, thereby obtaining lower bounds on the guaranteed error correction capability. We explore the relationship between a class of graphs known as cage graphs and trapping sets to establish upper bounds on the error correction capability.We also propose an algorithm to identify the most probable noise configurations, also known as instantons, that lead to error floor for linear programming (LP) decoding over the BSC. With the insight gained from the above analysis techniques, we propose novel code construction techniques that result in codes with superior error floor performance.