IET International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN)

An improved normalized BP based decoding algorithm for LDPC codes

Haigang Zhang, Dongfeng Yuan, Cheng‐Xiang Wang

Published Jan 1, 2008• 5 citations

Abstract

A uniformly most powerful (UMP) belief propagation (BP) based algorithm is referred to as a simplified version of a BP algorithm with reduced complexity but performance loss for low density parity check (LDPC) codes. To compensate the performance loss, the normalized BP based algorithm was proposed, where the normalization factor was derived by mean ratio or by minimizing the mean square error. In this paper, an improved novel normalized BP based algorithm is proposed. The normalization uses multiplicative factor instead of divisional factor. The novel scheme shows better performance than the existing normalized BP based algorithms while keeping the same implementation complexity. The simulation is done for two kinds of LDPC codes: random constructed codes and finite geometry codes. At high signal-to-noise ratio (SNR) region, the proposed scheme can achieve even better performance than BP algorithm for short length random constructed LDPC codes.

DOI: 10.1049/cp:20080977

Related papers

An Improvement of UMP-BP Decoding Algorithm Using the Minimum Mean Square Error Linear Estimator

Namshik Kim, Jaebum Kim, Hyuncheol Park +1 more

In this paper, we propose the modified uniformly most powerful (UMP) belief-propagation (BP)-based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP-based algorithm. This modified UMP BP-based algorithm shows better performance than that of the normalized UMP BP-based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP-based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP-based algorithm.

ETRI JournalOct 13, 20041 citation

An Improved Decoding Algorithm for LDPC Codes

XU Yong-tai

An improved decoding algorithm based on minimum mean square error(MMSE) for low-density parity-check(LDPC) codes is proposed.The new algorithm combines processing methods of two modified uniformly most powerful(UMP) BP-based algorithms(normalized UMP BP-based algorithm and offset UMP BP-based algorithm) and attains the corresponding normalization factor and offset factor according to MMSE criterion.Simulations indicate that,compared with the two above-mentioned algorithms,a gain of 0.03 dB can be achieved at bit error rate(BER) of 10-6 with the proposed algorithm when a(3,6) regular LDPC code of code length 1 008 and code rate 1/2 is used.

Beijing Youdian Xueyuan xuebaoJan 1, 20070 citations

A Classified Normalized BP-Based Algorithm with 2-Dimensional Correction for LDPC Codes

Zhou Zhong, Shuming Guo, Xiangyang Xu +1 more

A BP-based algorithm with 2-dimensional classified normalized correction is developed to reduce the complexity and improve the performance of decoding algorithm for the low density parity check (LDPC) codes. The algorithm first utilizes classification according to the absolute values of incoming messages in check nodes. Then it uses 2-dimensional normalization to correct the minimum and sub-minimum values. The 2-dimensional normalized factors can be calculated respectively by using probability and statistic theory in the initialization step. Simulation results illustrate that the proposed algorithm achieves better performance in bit error ratio (BER) and average iteration number than normalized BP-based algorithm in the high signal noise ratio (SNR) region, i.e., it can achieve 0.4 dB SNR gain and reduce 20% number of iterations at BER= 5 10  , whose complexity is also much less than that of belief propagation (BP) algorithm. It is concluded that the proposed algorithm offers better tradeoff between performance and complexity.  Index Terms—channel coding theory, density evolution, Iterative decoding algorithm

Journal of CommunicationsJan 1, 20135 citations

A high‐performance belief propagation decoding algorithm for codes with short cycles

Ali Karimi-lenji, Monireh Houshmand, Fahimeh Zarmehi

Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.

International Journal of Communication SystemsFeb 28, 20172 citations

Very-low-complexity decoding algorithm for low-density parity-check codes

Hui-Shi Song, Ping Zhang

In this paper, we propose a new very-low-complexity (VLC) decoding algorithm for low-density parity-check (LDPC) codes. This new algorithm only need subtract a correcting factor to improve the accuracy of the soft message values delivered by a previously proposed simplified belief-propagation (BP)-based algorithm called uniformly most powerful (UMP) algorithm. However, the VLC algorithm outperforms the UMP algorithm greatly. The correcting factor can be obtained by simulation or theoretical derivation. This new VLC algorithm is much simpler to implement than BP algorithm, and also simpler than previously proposed normalized BP-based algorithm in which the operation of dividing a normalization factor is needed. However, the performance of the VLC algorithm is slightly better than that of the normalized BP-based algorithm. Also, the VLC algorithm is universal in that the decoding is independent of the channel characteristics. Simulation results demonstrate that the performance of the new VLC algorithm can nearly approach that of BP on the additive white Gaussian noise (AWGN) channel.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)Jul 8, 20042 citations

Near optimum universal belief propagation based decoding of low-density parity check codes

Jinghu Chen, M.P.C. Fossorier

In this paper, we propose a belief-propagation (BP)-based decoding algorithm which utilizes normalization to improve the accuracy of the soft values delivered by a previously proposed simplified BP-based algorithm. The normalization factors can be obtained not only by simulation, but also, importantly, theoretically. This new BP-based algorithm is much simpler to implement than BP decoding as it requires only additions of the normalized received values and is universal, i.e., the decoding is independent of the channel characteristics. Some simulation results are given, which show this new decoding approach can achieve an error performance very close to that of BP on the additive white Gaussian noise channel, especially for low-density parity check (LDPC) codes whose check sums have large weights. The principle of normalization can also be used to improve the performance of the max-log-MAP algorithm in turbo decoding, and some coding gain can be achieved if the code length is long enough.

IEEE Transactions on CommunicationsMar 1, 2002661 citations