Abstract
A modified message propagation algorithm is proposed for a low-complexity decoder of low-density parity-check (LDPC) codes, which controls the information propagated from variable and check nodes.The proposed threshold-based node deactivation for variable nodes and zero-forcing scheme for check nodes remarkably reduce decoding complexity required for similar error performance. In the proposed scheme, different thresholds, which are determined from the base matrix of the LDPC codes, are applied for each type of variable node. In addition, thresholds for deactivating variable nodes are increased while the decoding process is operated for a reduction in decoding complexity without early error floor, which is a drawback of the conventional threshold-based deactivation scheme. Simulation results show that the proposed scheme enables normalized min-sum decoders to decode successfully with less complexity than the conventional threshold-based deactivating scheme.
Highlights
Low-density parity-check (LDPC) codes were first introduced by Gallager [1] in 1962 and rediscovered by Mackay [2] in 1999
In the viewpoint of error performance, LDPC codes exhibit better behavior for high code rates compared to turbo codes, which the last are better for lower code rates
Since this paper is the research for LDPC codes, only schemes for LDPC codes are briefly introduced but there were various researches for practical usage of turbo codes [4,5,6,7]
Summary
Low-density parity-check (LDPC) codes were first introduced by Gallager [1] in 1962 and rediscovered by Mackay [2] in 1999. The original iterative decoding algorithm for LDPC codes, known as the sum-product algorithm [2], shows good error correction performance, its computational complexity is quite high. Savin proposed a self-correction (SC) method for improving performance of min-sum algorithm [11, 12]. He checked the sign changes of the messages from variable nodes between two consecutive iterations to identify unreliable messages. The cost of the performance improvement is the additional memory required to store the signs of passing messages This overhead can be relieved by changing the rules of the conventional SC method [16]. We introduce a low-complexity decoding scheme that reduces the number of activated nodes.
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