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IEICE Technical Report; IEICE Tech. Rep.

Investigation of Application of Min-Sum Algorithm on Block Error Rate Performance of Polar Codes

Nobuhiko Miki, Satoshi Suyama, Satoshi Nagata

Published Jan 16, 2020• 0 citations
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Modified min-sum SCAN decoding algorithm for polar codes

Yuan Fang, Bin Tian

中国邮电高校学报：英文版•Dec 30, 2018•0 citations

Performance Analysis of Different Flexible Decoding Algorithms for NR-LDPC Codes

Layla Mahdi Salih, Thuraya Mahmood Qaradaghi, Jalal J. Hamad Ameen

Channel coding technique is a fundamental building block in any modern communication system to realize reliable, fast, and secure data transmission. At the same time, it is a challenging and crucial task, as the data transmission happens in a channel where noise, fading, and other impairments are present. The Low-Density Parity-Check (LDPC) codes give substantial results close to the Shannon limit when the complexity and processing delay time are unlimited. In this paper, the performance of the LDPC decoding with four algorithms was investigated. The investigated four algorithms were Belief Propagation (BP), Layered Belief Propagation (LBP), Normalized min-sum (NMS), and Offset min-sum (OMS). These algorithms were examined for code rates ranging from 1/3 to 9/10 and message block lengths (64, 512, 1024, and 5120) bits. The simulation results revealed the flexibility of these decoders in supporting these code rates and block lengths, which enables their usage in a wide range of applications and scenarios for fifth-generation (5G) wireless communication. In addition, the effect of the maximum number of decoding iterations on the error correction performance was investigated, and a gain of 5.6 dB can be obtained by using 32 decoding iterations at BER=2*10-3 instead of one decoding iteration. The results showed that the decoders performed better for longer message blocks than for short message blocks, and less power was required for transmitting longer messages. Finally, the comparison results of their performance in terms of bit error rate (BER) under the same conditions showed a gain of 0.8 dB using LBP at BER= 10-5 compared with the NMS decoding algorithm.

Tikrit Journal of Engineering Sciences•Dec 11, 2022•3 citations

Optimization and Hardware Implementation of Learning Assisted Min-Sum Decoders for Polar Codes

Ning Lyu, Bin Dai, Hongfei Wang +1 more

Journal of Signal Processing Systems•Jun 27, 2020•2 citations

A low-complexity high-performance decoding algorithm for fixed-point LDPC decoders

Jui-Hui Hung, Sau-Gee Chen

The bit-error-rate (BER) performance of an LDPC decoding algorithm will be seriously degraded when the algorithm is realized with fixed-point implementation in practical applications, due to the introduced quantization errors and rounding errors. To remedy the problem, this paper proposes an improved min-sum algorithm (MSA), called CMVP algorithm. It achieves better performances than the popular min-sum algorithm (MSA), under the condition of the same fixed-point precision. Besides, the hardware overhead of the new algorithm over conventional MSA is small. On the other hand, under the condition of comparable performances, MSA algorithm needs a higher fixed-point precision and hardware costs than the new CMVP algorithm, according to the simulations and hardware synthesis results. The new algorithm also works well in a wide range of code rates and code lengths.

International Conference on Signal Processing and Communication Systems•Dec 1, 2008•2 citations

On Implementation of Min-Sum Algorithm and Its Modifications for Decoding Low-Density Parity-Check (LDPC) Codes

J. Zhao, Farhad Zarkeshvari, Amir H. Banihashemi

The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.

IEEE Transactions on Communications•Apr 1, 2005•283 citations

Error Bounds of Polar Codes Under Varying Channel Conditions

Christopher Schnelling, Simon Görtzen, Anke Schmeink

Systems Communications•Mar 2, 2015•2 citations
Channel coding technique is a fundamental building block in any modern communication system to realize reliable, fast, and secure data transmission. At the same time, it is a challenging and crucial task, as the data transmission happens in a channel where noise, fading, and other impairments are present. The Low-Density Parity-Check (LDPC) codes give substantial results close to the Shannon limit when the complexity and processing delay time are unlimited. In this paper, the performance of the LDPC decoding with four algorithms was investigated. The investigated four algorithms were Belief Propagation (BP), Layered Belief Propagation (LBP), Normalized min-sum (NMS), and Offset min-sum (OMS). These algorithms were examined for code rates ranging from 1/3 to 9/10 and message block lengths (64, 512, 1024, and 5120) bits. The simulation results revealed the flexibility of these decoders in supporting these code rates and block lengths, which enables their usage in a wide range of applications and scenarios for fifth-generation (5G) wireless communication. In addition, the effect of the maximum number of decoding iterations on the error correction performance was investigated, and a gain of 5.6 dB can be obtained by using 32 decoding iterations at BER=2*10-3 instead of one decoding iteration. The results showed that the decoders performed better for longer message blocks than for short message blocks, and less power was required for transmitting longer messages. Finally, the comparison results of their performance in terms of bit error rate (BER) under the same conditions showed a gain of 0.8 dB using LBP at BER= 10-5 compared with the NMS decoding algorithm.
The bit-error-rate (BER) performance of an LDPC decoding algorithm will be seriously degraded when the algorithm is realized with fixed-point implementation in practical applications, due to the introduced quantization errors and rounding errors. To remedy the problem, this paper proposes an improved min-sum algorithm (MSA), called CMVP algorithm. It achieves better performances than the popular min-sum algorithm (MSA), under the condition of the same fixed-point precision. Besides, the hardware overhead of the new algorithm over conventional MSA is small. On the other hand, under the condition of comparable performances, MSA algorithm needs a higher fixed-point precision and hardware costs than the new CMVP algorithm, according to the simulations and hardware synthesis results. The new algorithm also works well in a wide range of code rates and code lengths.
The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.