NDLI: A Modified Optimally Quantized Offset Min-Sum Decoding Algorithm for Low-Complexity LDPC Decoder

Content Provider	Springer Nature Link
Author	Roberts, Michaelraj Kingston Jayabalan, Ramesh
Copyright Year	2014
Abstract	In this paper, A Modified Optimally Quantized Offset Min-Sum Decoding Algorithm for low complexity, low-density parity-check decoder is proposed. In the proposed algorithm, more effective adjustment for the check node and variable node updating process is achieved by introducing optimally quantized adaptive offset correction factors. Unlike the conventional Offset Min-Sum algorithm and its variants, the offset correction factor of the proposed algorithm can self adapt itself according to the state of both the check node and variable node during each iteration. Simulation results show that the proposed decoding algorithm achieves significant coding gain improvement over other Min-Sum algorithms without additional hardware complexity. In addition to the coding gain improvement, the proposed algorithm can reducing up to 8–14 % of the decoding iterations required to correct the channel errors at relatively low Signal-to-noise ratio. Furthermore, in the proposed algorithm the finite word length effects are reduced by utilizing a 6-bit non-uniform quantization scheme. The proposed algorithm with a 6-bit non-uniform quantization scheme is shown to achieve about 21.6 % reduction in the total number of memory bits required to store the processed data without degradation in the decoding performance.
Starting Page	561
Ending Page	570
Page Count	10
File Format	PDF
ISSN	09296212
Journal	Wireless Personal Communications
Volume Number	80
Issue Number	2
e-ISSN	1572834X
Language	English
Publisher	Springer US
Publisher Date	2014-08-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coding gain Finite word length Low-density parity-check (LDPC) codes Min-Sum algorithm (MSA) Quantization scheme Communications Engineering, Networks Signal, Image and Speech Processing Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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