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ACM Great Lakes Symposium on VLSI (GLSVLSI)

Parallel turbo-sum-product decoder architecture for quasi-cyclic LDPC codes

Yongmei Dai, Zhiyuan Yan, Ning Chen

Published Apr 30, 2006• 3 citations
Publisher

Abstract

In this paper, we first propose a parallel turbo-sum-product (PTSP) decoding algorithm for quasi-cyclic (QC) low-density parity-check codes, and show that our proposed algorithm achieves better error performance and faster convergence than the sum-product (SP) or overlapped SP (OSP) algorithm. Based on our PTSP algorithm, we then propose a partly parallel decoder architecture, which requires smaller memory size, less memory access and power consumption, and simpler control than decoder architectures based on the OSP algorithm.

DOI
10.1145/1127908.1127980

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In this paper, we first propose a parallel turbo-sum-product (PTSP) decoding algorithm for quasi-cyclic (QC) low-density parity-check codes, and show that our proposed algorithm achieves better error performance and faster convergence than the sum-product (SP) or overlapped SP (OSP) algorithm. Based on our PTSP algorithm, we then propose a partly parallel decoder architecture, which requires smaller memory size, less memory access and power consumption, and simpler control than decoder architectures based on the OSP algorithm.
In this paper, we first propose parallel turbo-sum-product (PTSP) and turbo-shuffled-sum-product (TSSP) decoding algorithms for partly parallel decoder architectures of quasi-cyclic (QC) low-density parity-check (LDPC) codes. Our proposed algorithms not only achievefasterconvergenceandbettererrorperformancethan the sum-product (SP) decoding algorithm, but also needlessmemoryin implementation. Then we propose a partly parallel decoder architecture based on our PTSP algorithm and implement it using FPGA. Our PTSP decoder architecture achievessignificantlyhigherthroughputand requireslessmemorythan previously proposed decoder architectures with the same FPGA and LDPC code. Finally, to further reduce the memory requirement, we also propose a partly parallel decoder architecture based on our TSSP algorithm.
In this paper, we first propose a turbo-sum-product (TSP) decoding algorithm for quasi-cyclic (QC) low-density parity-check (LDPC) codes and show that our proposed algorithm not only achieves faster convergence and better error performances than the sum-product (SP) algorithm, but also needs less memory in implementation. Compared with the turbo decoding algorithm, our TSP algorithm saves the same amount of memory and achieves a better tradeoff between sub-iterations and convergence rate, leading to a higher throughput. We also propose a shuffled-sum-product (SSP) decoding algorithm, which is the dual of the TSP algorithm.
We propose turbo-sum-product (TSP) and shuffled-sum-product (SSP) decoding algorithms for quasi-cyclic low-density parity-check codes, which not only achieve faster convergence and better error performance than the sum-product algorithm, but also require less memory in partly parallel decoder architectures. Compared with the turbo decoding algorithm, our TSP algorithm saves the same amount of memory and may achieve a higher decoding throughput. The convergence behaviors of our TSP and SSP algorithms are also compared with those of the SP, turbo, and shuffled algorithms by their extrinsic information transfer (EXIT) charts.
In this paper, we propose partly parallel architectures based on optimal overlapped sum-product (OSP) decoding. To ensure high throughput and hardware utilization efficiency, partly parallel parity check and pipelined access to memory are utilized. Impacts of different node update algorithms and quantization schemes are studied. FPGA implementation of our proposed architectures for a (1536.768) (3.6)-regular QC LDPC code can achieve an estimated 61 Mbps decoding throughput at SNR=4.5dB. Finally, noncoherent OSP decoder, which does not always satisfy the data dependency constraints, is proposed to ensure that the maximum throughput gain 2 of the OSP decoding is achieved for all QC LDPC codes
In this paper, the authors propose an efficient highly-parallel decoder architecture using partially overlapped decoding scheme for quasi-cyclic (QC) low-density parity-check (LDPC) codes, which leads to reduction in hardware complexity and power consumption. Generally, due to the regularly structured parity-check matrix H of QC LDPC codes, the message updating computations in the check node unit (CNU) and the variable node unit (VNU) can be efficiently overlapped, which increases the decoding throughput by maximizing the hardware utilization efficiency (HUE). However, the partially overlapped decoding scheme cannot be used to design a highly-parallel decoding architecture for high-throughput applications. For (3, 5)-regular QC LDPC codes, our proposed method could reduce the hardware complexity by approximately 33% for the CNU and 20% for the VNU in the highly-parallel decoder architecture without any performance degradation. In addition, the power consumption can be minimized by reducing the total number of memory accesses for updated messages.
In this paper, an area efficient partially parallel decoder architecture suited for (modified) min-sum decoding algorithm for general high rate quasi-cyclic low-density parity-check (QC-LDPC) codes is proposed. To reduce the hardware complexity for parallel processing, an efficient data scheduling unit is presented. The optimized partially parallel decoder architecture can linearly increase the decoding throughput with small hardware overhead. Typically, over 30% of memory can be saved with the architecture presented in this paper. Consequently, the proposed approach facilitates the applications of LDPC codes in area/power sensitive high speed communication systems.