Layered approximately regular (LAR) low-density parity-check (LDPC) codes are proposed, with which one single pair of encoder and decoder support various code lengths and code rates. The parity check matrices of LAR-LDPC codes have a "layer-block-cell" structure with some additional constraints. An encoder architecture is then designed for LAR-LDPC codes, by making two improvements to the Richardson-Urbanke approach: the forward substitution operation is entirely removed and the dense-matrix-vector multiplication is handled using feedback shift-registers. A partially parallel decoder architecture is also designed for LAR-LDPC codes, where a layered modified min-sum decoding algorithm is used to trade off among complexity, speed, and performance. More importantly, the interconnection network, which is inevitable for partially parallel decoders, has much lower hardware complexity compared with that for general LDPC codes. Both the encoder and decoder architectures are highly flexible in code length and code rate.
Layered approximately regular (LAR) low-density parity-check (LDPC) codes are proposed, with which one single pair of encoder and decoder support various code lengths and code rates. The parity check matrices of LAR-LDPC codes have a "layer-block-cell" structure with some additional constraints. An encoder architecture is then designed for LAR-LDPC codes, by making two improvements to the Richardson-Urbanke approach: the forward substitution operation is entirely removed and the dense-matrix-vector multiplication is handled using feedback shift-registers. A partially parallel decoder architecture is also designed for LAR-LDPC codes, where a layered modified min-sum decoding algorithm is used to trade off among complexity, speed, and performance. More importantly, the interconnection network, which is inevitable for partially parallel decoders, has much lower hardware complexity compared with that for general LDPC codes. Both the encoder and decoder architectures are highly flexible in code length and code rate.
Low-density parity-check (LDPC) codes have been shown to have good error correcting performance approaching Shannon’s limit. Good error correcting performance enables efficient and reliable communication. However, a LDPC code decoding algorithm needs to be executed efficiently to meet cost, time, power and bandwidth requirements of target applications. The constructed codes should also meet error rate performance requirements of those applications. Since their rediscovery, there has been much research work on LDPC code construction and implementation. LDPC codes can be designed over a wide space with parameters such as girth, rate and length. There is no unique method of constructing LDPC codes. Existing construction methods are limited in some way in producing good error correcting performing and easily implementable codes for a given rate and length. There is a need to develop methods of constructing codes over a wide range of rates and lengths with good performance and ease of hardware implementability. LDPC code hardware design and implementation depend on the structure of target LDPC code and is also as varied as LDPC matrix designs and constructions. There are several factors to be considered including decoding algorithm computations,processing nodes interconnection network, number of processing nodes, amount of memory, number of quantization bits and decoding delay. All of these issues can be handled in several different ways. \nThis thesis is about construction of LDPC codes and their hardware implementation. LDPC code construction and implementation issues mentioned above are too many to be addressed in one thesis. The main contribution of this thesis is the development of LDPC code construction methods for some classes of structured LDPC codes and techniques for reducing decoding time. We introduce two main methods for constructing structured codes. In the first method, column-weight two LDPC codes are derived from distance graphs. A wide range of girths, rates and lengths are obtained compared to existing methods. The performance and implementation complexity of obtained codes depends on the structure of their corresponding distance graphs. In the second method, a search algorithm based on bit-filing and progressive-edge growth algorithms is introduced for constructing quasi-cyclic LDPC codes. The algorithm can be used to form a distance or Tanner graph of a code. This method could also obtain codes over a wide range of parameters. Cycles of length four are avoided by observing the row-column constraint. \nRow-column connections observing this condition are searched sequentially or randomly. Although the girth conditions are not sufficient beyond six, larger girths codes were easily obtained especially at low rates. The advantage of this algorithm compared to other methods is its flexibility. It could be used to construct codes for a given rate and length with girths of at least six for any sub-matrix configuration or rearrangement. The code size is also easily varied by increasing or decreasing sub-matrix size. Codes obtained using a sequential search criteria show poor performance at low girths (6 and 8) while random searches result in good performing codes. \nQuasi-cyclic codes could be implemented in a variety of decoder architectures. One of the many options is the choice of processing nodes interconnect. We show how quasi-cyclic codes processing could be scheduled through a multistage network. Although these net-works have more delay than other modes of communication, they offer more flexibility at a reasonable cost. Banyan and Benes networks are suggested as the most suitable networks. \nDecoding delay is also one of several issues considered in decoder design and implementation. In this thesis, we overlap check and variable node computations to reduce decoding time. Three techniques are discussed, two of which are introduced in this thesis. The techniques are code matrix permutation, matrix space restriction and sub-matrix row-column scheduling. Matrix permutation rearranges the parity-check matrix such that rows and columns that do not have connections in common are separated. This techniques can be applied to any matrix. Its effectiveness largely depends on the structure of the code. We show that its success also depends on the size of row and column weights. Matrix space restriction is another technique that can be applied to any code and has fixed reduction in time or amount of overlap. Its success depends on the amount of restriction and may be traded with performance loss. The third technique already suggested in literature relies on the internal cyclic structure of sub-matrices to achieve overlapping. The technique is limited to LDPC code matrices in which the number of sub-matrices is equal to row and column weights. We show that it can be applied to other codes with a lager number of sub-matrices than code weights. However, in this case maximum overlap is not guaranteed. We calculate the lower bound on the amount of overlapping. Overlapping could be applied to any sub-matrix configuration of quasi-cyclic codes by arbitrarily choosing \nthe starting rows for processing. Overlapping decoding time depends on inter-iteration waiting times. We show that there are upper bounds on waiting times which depend on the code weights. Waiting times could be further reduced by restricting shifts in identity sub-matrices or using smaller sub-matrices. This overlapping technique can reduce the decoding time by up to 50% compared to conventional message and computation scheduling. \nTechniques of matrix permutation and space restriction results in decoder architectures that are flexible in LDPC code design in terms of code weights and size. This is due to the fact that with these techniques, rows and columns are processed in sequential order to achieve overlapping. However, in the existing technique, all sub-matrices have to be processed in parallel to achieve overlapping. Parallel processing of all code sub-matrices requires the architecture to have the number of processing units at least equal to the number sub-matrices. Processing units and memory space should therefore be distributed among the sub-matrices according to the sub-matrices arrangement. This leads to high complexity or inflexibility in the decoder architecture. We propose a simple, programmable and high throughput decoder architecture based on matrix permutation and space restriction techniques.
Joint (3,k) -Regular LDPC Code and Decoder/Encoder Design
Recently, low-density parity-check (LDPC) codes have attracted a lot of attention in the coding theory community. However, their real-world applications are still problematic mainly due to the lack of effective decoder/encoder hardware design approaches. In this paper, we present a joint (3,k)-regular LDPC code and decoder/encoder design technique to construct a class of (3,k)-regular LDPC codes that not only have very good error-correcting capability but also exactly fit to high-speed partly parallel decoder and low-complexity encoder implementations. We also develop two techniques to further modify this joint design scheme to achieve more flexible tradeoffs between decoder hardware complexity and decoding speed.
In this paper, a combined Low-Density Parity-Check (LDPC) code decoding design method, called modified Block-Type LDPC (B-LDPC), for realistic LDPC coding system architectures is presented. The B-LDPC code, which is a special class of quasi-cyclic LDPC (QC-LDPC), has an efficient encoding algorithm owing to the simple structure of their parity-check matrices. A proposed distribution of irregular parity-check matrix for the modified B-LDPC is developed so that we can obtain an area-efficient decoder design, good error correction performance, and achievable architecture implementation. The modified B-LDPC code decoding utilizes the iterative min-sum algorithm (MSA) and its decoding architecture design employs the non-overlapping operations of bit node unit (BNU) and check node unit (CNU). Different block matrix sizes for parity-check matrix can be adopted so that the modified B-LDPC code decoding improves the throughput without obvious performance degradation.
Because of their excellent error-correcting performance, low-density parity-check (LDPC) codes have recently attracted a lot of attention. In this paper, we are interested in the practical LDPC code decoder hardware implementations. The direct fully parallel decoder implementation usually incurs too high hardware complexity for many real applications, thus partly parallel decoder design approaches that can achieve appropriate trade-offs between hardware complexity and decoding throughput are highly desirable. Applying a joint code and decoder design methodology, we develop a high-speed -regular LDPC code partly parallel decoder architecture based on which we implement a -bit, rate- -regular LDPC code decoder on Xilinx FPGA device. This partly parallel decoder supports a maximum symbol throughput of Mbps and achieves BER at 2 dB over AWGN channel while performing maximum decoding iterations.
In the past few years, Gallager's low-density parity-check (LDPC) codes received a lot of attention and many efforts have been devoted to analyzing and improving their error-correcting performance. However, little consideration has been given to the LDPC decoder VLSI implementation. The straightforward fully parallel decoder architecture usually incurs too high complexity for many practical purposes and should be transformed to a partly parallel realization. Unfortunately, due to the randomness of LDPC codes, it is nearly impossible to develop an effective transformation for an arbitrarily given LDPC code. We propose a joint code and decoder design approach to construct a class of (3, k)-regular LDPC codes which exactly fit a partly parallel decoder implementation and have a very good performance. Moreover, for such LDPC codes, we propose a systematic, efficient encoding scheme by effectively exploiting the sparseness of its parity check matrix.
There are many low-density parity-check (LDPC) decoder architectures in the literature most of which are based on structured codes. Besides been specific to a particular class of codes, suggested architectures are limited in scalability. The major challenge in decoder design and implementation is the consideration of several strongly interrelated factors. These factors affect decoder computation and communication complexity, and error-correcting performance. In this paper we present a low-complexity, flexible and scalable LDPC decoder architecture for quasi-cyclic codes that supports multiple code designs (size, weights, rates, regular and irregular). The architecture is a result of a combination of flexible code construction and computation overlapping techniques. The architecture is based on overlapping techniques that allow sequential processing of check and variable nodes. The overlapping techniques lead to a low-complexity decoder interconnect compared to the existing techniques as they allow serial processing of code sub-matrices as compared to parallel processing of all sub-matrices in the existing overlapping technique. Although the architecture is based on quasi-cyclic codes, it can also be applied to random or other structured codes making it the most flexible, scalable and high-throughput partly-parallel LDPC decoder architecture.