Low-Density Parity-check (LDPC) codes have attracted considerable attention due to their capacity approaching performance over AWGN channel and highly parallelizable decoding schemes. They have been considered in a variety of industry standards for the next generation communication systems. In general, LDPC codes achieve outstanding performance with large codeword lengths (e.g., N>1000 bits), which lead to a linear increase of the size of memory for storing all the soft messages in LDPC decoding. In the next generation communication systems, the target data rates range from a few hundred Mbit/sec to several Gbit/sec. To achieve those very high decoding throughput, a large amount of computation units are required, which will significantly increase the hardware cost and power consumption of LDPC decoders. LDPC codes are decoded using iterative decoding algorithms. The decoding latency and power consumption are linearly proportional to the number of decoding iterations. A decoding approach with fast convergence speed is highly desired in practice.
This thesis considers various VLSI design issues of LDPC decoder and develops efficient approaches for reducing memory requirement, low complexity implementation, and high speed decoding of LDPC codes. We propose a memory efficient partially parallel decoder architecture suited for quasi-cyclic LDPC (QC-LDPC) codes using Min-Sum decoding algorithm. We develop an efficient architecture for general permutation matrix based LDPC codes. We have explored various approaches to linearly increase the decoding throughput with a small amount of hardware overhead. We develop a multi-Gbit/sec LDPC decoder architecture for QC-LDPC codes and prototype an enhanced partially parallel decoder architecture for a Euclidian geometry based LDPC code on FPGA. We propose an early stopping scheme and an extended layered decoding method to reduce the number of decoding iterations for undecodable and decodable sequence received from channel. We also propose a low-complexity optimized 2-bit decoding approach which requires comparable implementation complexity to weighted bit flipping based algorithms but has much better decoding performance and faster convergence speed
