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IEEE Communications Letters

A Relaxed Min-Sum LDPC Decoder With Simplified Check Nodes

Saied Hemati, François Leduc-Primeau, Warren J. Gross

Published Jan 12, 2016• 25 citations
Publisher

Abstract

This letter presents a heuristic technique for simplifying the parity-check node operation in a relaxed min-sum iterative decoder. The proposed decoder eliminates the second-minimum computation in check nodes, which allows broadcasting the same output to all neighboring variable nodes to alleviate routing problem in VLSI implementations of low-density parity check (LDPC) decoders. The second-minimum, when required, is emulated by adding an offset to the first-minimum. The proposed relaxed decoder also uses a relaxation factor equal to 0.5 to simplify variable nodes. Simulation results for two LDPC codes show the proposed decoding algorithm with only 4-bit quantization closely matches the performance of floating-point normalized/offset min-sum and sum-product decoders in the waterfall region.

DOI
10.1109/lcomm.2016.2517142

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This letter presents a heuristic technique for simplifying the parity-check node operation in a relaxed min-sum iterative decoder. The proposed decoder eliminates the second-minimum computation in check nodes, which allows broadcasting the same output to all neighboring variable nodes to alleviate routing problem in VLSI implementations of low-density parity check (LDPC) decoders. The second-minimum, when required, is emulated by adding an offset to the first-minimum. The proposed relaxed decoder also uses a relaxation factor equal to 0.5 to simplify variable nodes. Simulation results for two LDPC codes show the proposed decoding algorithm with only 4-bit quantization closely matches the performance of floating-point normalized/offset min-sum and sum-product decoders in the waterfall region.
This paper proposes a low-complexity LDPC decoding algorithm with simplified check nodes updating. The proposed algorithm simplifies the result of the second-minimum in check nodes based on the first-minimum computation instead of computing it directly. In order to obtain the approximate result, effective corrected coefficients are utilized, which can reduce the complexity by eliminating the complex computations. Complexity analysis is provided and the results indicate that the complexity of the proposed algorithm is much lower than the general NMS algorithm and MS-based simplified algorithms. Simulation results show that the performance of the proposed algorithm can closely match the NMS algorithm with the same number of iterations.
This paper proposes a low-complexity min-sum algorithm for decoding low-density parity-check codes. It is an improved version of the single-minimum algorithm where the two-minimum calculation is replaced by one minimum calculation and a second minimum emulation. In the proposed one, variable correction factors that depend on the iteration number are introduced and the second minimum emulation is simplified, reducing by this way the decoder complexity. This proposal improves the performance of the single-minimum algorithm, approaching to the normalized min-sum performance in the water-fall region. Also, the error-floor region is analyzed for the code of the IEEE 802.3an standard showing that the trapping sets are decoded due to a slow down of the convergence of the algorithm. An error-floor free operation below BER=10-15is shown for this code by means of a field-programmable gate array (FPGA)-based hardware emulator. A layered decoder is implemented in a 90-nm CMOS technology achieving 12.8 Gbps with an area of 3.84 mm2.
In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.
Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
Low-density parity-check codes are widely used in communication systems. To meet the high throughput and energy efficiency requirements of current and future systems, it is desirable to further simplify the decoder. Quantized min-sum (MS) decoders are of particular interest for their low implementation complexity, which can be further reduced by computing a single minimum (SM) during check node update, instead of two. However, this simplification can lead to poor decoding performance unless it is carefully incorporated. In this paper, we formalize a general optimization problem for SM decoding, and propose search heuristics to solve it. In addition, we provide density evolution (DE) equations for the first two decoding iterations that properly take into account the lack of extrinsic update rule, and show that this DE result can be used to obtain good solutions to the SM optimization problem with low computational complexity.
Abstract ― Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.