Channel coding plays a vital role in telecommunication. Low-Density Parity- Check (LDPC) codes are linear error-correcting codes. According to the 3rd Generation Partnership Project (3GPP) TS 38.212, LDPC is recommended for the Fifth-generation (5G) New Radio (NR) shared channels due to its high throughput, low latency, low decoding complexity and rate compatibility. LDPC encoding chain has been defined in 3GPP TS 38.212, but some details of LDPC encoding chain are still required to be explored in the MATLAB environment. For example, how to deal with the filler bits for encoding and decoding. However, as the reverse process of LDPC encoding, there is no information on LDPC decoding process for 5G NR shared channels in 3GPP TS 38.212. In this thesis project, LDPC encoding and decoding chains were thoughtfully developed with MATLAB programming based on 3GPP TS 38.212. Several LDPC decoding algorithms were implemented and optimized. The performance of LDPC algorithms was evaluated using block error rate (BLER) v.s. signal to noise ratio (SNR) and CPU time. Results show that the double diagonal structure-based encoding method is an efficient LDPC encoding algorithm for 5G NR. Layered Sum Product Algorithm (LSPA) and Layered Min-Sum Algorithm (LMSA) are more efficient than Sum Product Algorithm (SPA) and Min-Sum Algorithm (MSA). Layered Normalized Min-Sum Algorithm (LNMSA) with proper normalization factor and Layered Offset Min-Sum Algorithm (LOMSA) with good offset factor can optimize LMSA. The performance of LNMSA and LOMSA decoding depends more on code rate than transport block. / Kanalkodning spelar en viktig roll i telekommunikation. Paritetskontrollkoder med låg densitet (LDPC) är linjära felkorrigeringskoder. Enligt tredje generationens partnerskapsprojekt (3GPP) TS 38.212, LDPC rekommenderas för den femte generationens (5G) nya radio (NR) delade kanal på grund av dess höga genomströmning, låga latens, låga avkodningskomplexitet och hastighetskompatibilitet. LDPC kodningskedjan har definierats i 3GPP TS 38.212, men vissa detaljer i LDPC kodningskedjan krävs fortfarande för att utforskas i Matlabmiljön. Till exempel hur man hanterar fyllnadsbitar för kodning och avkodning. Men som den omvända processen för LDPC kodning finns det ingen information om LDPC avkodningsprocessen för 5G NR delade kanaler på 3GPP TS 38.212. I detta avhandlingsprojekt utvecklades LDPC-kodning och avkodningskedjor enligt 3GPP TS 38.212. Flera LDPC-avkodningsalgoritmer implementerades och optimerades. Prestandan för LDPC-algoritmer utvärderades med användning av blockfelshalt (BLER) v.s. signal / brusförhållande (SNR) och CPU-tid. Resultaten visar att den dubbla diagonala strukturbaserade kodningsmetoden är en effektiv LDPC kodningsalgoritm för 5G NR. Layered Sum Product Algorithm (LSPA) och Layered Min-Sum Algorithm (LMSA) är effektivare än Sum Product Algorithm (SPA) och Min-Sum Algorithm (MSA). Layered Normalized Min-Sum Algorithm (LNMSA) med rätt normaliseringsfaktor och Layered Offset Min-Sum Algorithm (LOMSA) med bra offsetfaktor kan optimera LMSA. Prestandan för LNMSA- och LOMSA-avkodning beror mer på kodhastighet än transportblock.

This dissertation studies methods to achieve reliable communication over unreliable channels. Iterative decoding algorithms for low-density parity-check (LDPC) codes and generalized LDPC (GLDPC) codes are analyzed. A new class of error-correcting codes to enhance the reliability of the communication for high-speed systems, such as optical communication systems, is proposed. The class of spatially-coupled GLDPC codes is studied, and a new iterative hard- decision decoding (HDD) algorithm for GLDPC codes is introduced. The main result is that the minimal redundancy allowed by Shannon’s Channel Coding Theorem can be achieved by using the new iterative HDD algorithm with spatially-coupled GLDPC codes. A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with iterative decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. To the best of our knowledge, this is the first system that can approach the channel capacity using iterative HDD. The optimality of a codeword returned by the weighted min-sum (WMS) algorithm, an iterative decoding algorithm which is widely used in practice, is studied as well. The attenuated max-product (AttMP) decoding and weighted min-sum (WMS) decoding for LDPC codes are analyzed. Applying the max-product (and belief- propagation) algorithms to loopy graphs are now quite popular for best assignment problems. This is largely due to their low computational complexity and impressive performance in practice. Still, there is no general understanding of the conditions required for convergence and/or the optimality of converged solutions. This work presents an analysis of both AttMP decoding and WMS decoding for LDPC codes which guarantees convergence to a fixed point when a weight factor, β, is sufficiently small. It also shows that, if the fixed point satisfies some consistency conditions, then it must be both a linear-programming (LP) and maximum-likelihood (ML) decoding solution.

Low-Density Parity-Check (LDPC) codes are linear error-correcting codes used to establish reliable communication between units on a noisy transmission channel in mobile telecommunications. LDPC algorithms detect and recover altered or corrupted message bits using sparse parity-check matrices in order to decipher messages correctly. LDPC codes have been shown to be fitting coding schemes for the fifth generation (5G) New Radio (NR), according to the third generation partnership project (3GPP).  TietoEvry, a consultant in telecom, has discovered that optimizations of LDPC decoding algorithms can be achieved/obtained with the use of a parallel computing platform called Compute Unified Device Architecture (CUDA), developed by NVIDIA. This platform utilizes the capabilities of a graphics processing unit (GPU) rather than a central processing unit (CPU), which in turn provides parallel computing. An optimized version of an LDPC decoding algorithm, the Min-Sum Algorithm (MSA), is implemented in CUDA and in C++ for comparison in terms of CPU execution time, to explore the capabilities that CUDA offers. The testing is done with a set of 12 sparse parity-check matrices and input-channel messages with different sizes. As a result, the CUDA implementation executes approximately 55% faster than a standard, unoptimized C++ implementation.