Low Hardware Layered Decoding Architecture for LDPC Code
Journal: International Journal of Science and Research (IJSR) (Vol.2, No. 3)Publication Date: 2013-03-05
Authors : N. Aravind; L. Praveen Kumar;
Page : 1-4
Keywords : Decoding; field-programmable gate array; FPGA; forward error correction; low density parity check; LDPC;
Abstract
Low density parity check (LDPC) codes have been extensively adopted in next-generation forward error correction applications because they achieve very good performance using the iterative decoding approach of the belief-propagation (BP). The basic decoder design for achieving the highest decoding throughput is to allocate processors corresponding to all check and variable nodes, together with an interconnection network. In this fully-parallel decoder architecture, the hardware complexity due to the routing overhead is very large. Therefore, much of the work on LDPC decoder design has been directed towards achieving optimal tradeoffs between hardware complexity and decoding throughput. In particular, a time-multiplexed or folded approach, which is known as partially parallel decoder architecture, has been proposed. Low hardware layered decoding architecture for LDPC code scheme is proposed using only one switch network with direct connections. This method requires only one shuffle network, rather than the two shuffle networks which are used in conventional designs. In addition, this project can be extended to block parallel decoding scheme by suitably mapping between required memory banks and processing units in order to increase the decoding throughput.
Other Latest Articles
- Monitoring the Node due to the Effect of Packet Droppers and Modifiers in Wireless Sensor Network
- FPGA Implementation of Codec Design for Optimal Code Rate Crosstalk Avoidance Codes
- Ensuring Security Services for Data Storing and Data Sharing in Cloud Computing
- Factors Responsible for High and Low Motivational Level of University Academicians
- Automatic Distribution of Documents into Different Categories using Active Learning
Last modified: 2021-06-30 20:14:29