VLSI Design of a High-Throughput Multi-Rate Decoder for Structured LDPC Codes
DSD '05 Proceedings of the 8th Euromicro Conference on Digital System Design
Parallel turbo-sum-product decoder architecture for quasi-cyclic LDPC codes
GLSVLSI '06 Proceedings of the 16th ACM Great Lakes symposium on VLSI
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Joint (3,k)-regular LDPC code and decoder/encoder design
IEEE Transactions on Signal Processing
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Quasicyclic low-density parity-check codes from circulant permutation matrices
IEEE Transactions on Information Theory
LDPC block and convolutional codes based on circulant matrices
IEEE Transactions on Information Theory
Modified Shuffled Based Architecture for High-Throughput Decoding of LDPC Codes
Journal of Signal Processing Systems
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In this paper, we propose a low complexity decoder architecture for low-density parity-check (LDPC) codes using a variable quantization scheme as well as an efficient highly-parallel decoding scheme. In the sum-product algorithm for decoding LDPC codes, the finite precision implementations have an important tradeoff between decoding performance and hardware complexity caused by two dominant area-consuming factors: one is the memory for updated messages storage and the other is the look-up table (LUT) for implementation of the nonlinear function 驴(x). The proposed variable quantization schemes offer a large reduction in the hardware complexities for LUT and memory. Also, an efficient highly-parallel decoder architecture for quasi-cyclic (QC) LDPC codes can be implemented with the reduced hardware complexity by using the partially block overlapped decoding scheme and the minimized power consumption by reducing the total number of memory accesses for updated messages. For (3, 6) QC LDPC codes, our proposed schemes in implementing the highly-parallel decoder architecture offer a great reduction of implementation area by 33% for memory area and approximately by 28% for the check node unit and variable node unit computation units without significant performance degradation. Also, the memory accesses are reduced by 20%.