An improvement of approximate BP decoding

Tian Zhang

BP decoding algorithm is usually used to realize decoding of IRA codes,but the hardware circuit of BP decoding algorithm is complicated. A modified IRA decoding algorithm is proposed in order to have a good tradeoff between complexity and decoding performance. The proposed algorithm is approximate to BP decoding algorithm by the method of offset approximation ,which can simplify the complexity of BP decoding algorithm. The simulation results show that,compared with BP decoding algorithm,the modified IRA decoding algorithm can decrease algorithm complexity and keep good decoding performance. Compared with minimum-sum decoding algorithm,the complexity of modified IRA decoding algorithm is almost unchanged,but the decoding performance improves significantly.

Chen Yi-Ru, Huang–Chang Lee, Lin Mao-Chao

Steven S. Pietrobon, Robert Huijie Deng, A. Lafanechere +1 more

Chu-Hsiang Huang, Yao Li, Lara Dolecek

This paper analyzes the fundamental performance limits of an LDPC Belief Propagation (BP) decoder implemented on noisy hardware and proposes a robust decoder implementation to improve the resilience to hardware errors. Assuming that the effects of hardware noise in various computational units, i.e., variable nodes and check nodes, can be approximated by Gaussian noise, we develop a Gaussian approximate density evolution for noisy BP decoders. By the Gaussian approximate density evolution, we find that zero residual error rate is achievable for noisy BP decoders as long as the message representations are of arbitrarily high precision. Noisy BP decoding thresholds are then derived for various regular LDPC codes. These decoding thresholds determine maximum allowable communication and computation noise for reliable communication. Next, we propose an averaging BP decoder implementation by averaging over the messages in all up-to-date iterations to cancel out the computation noise. Simulation results demonstrate that on noisy hardware, the averaging BP decoder significantly reduces the residual error rates when compared to the nominal BP decoder.

Xue-lu DENG, Da-qin PENG

In recent years, the emerging Deep Learning (DL) technology has made progress in the field of decoding. Current polar code neural network decoder has faster convergence speed and better Bit Error Rate (BER) performance than Belief Propagation (BP) decoding. However, it still has the problem of high computational complexity. Therefore, in order to improve this problem, this paper adopts the idea of improving information update in the iterative process, and proposes a Recurrent Neural Network (RNN) Offset Min-Sum (OMS) BP decoding algorithm that improves Left information update (RNN-OMSBP-L) . The simulation results show that, compared with the Deep Neural Network (DNN) BP(DNN-BP) decoding algorithm, this algorithm replaces all multiplications with a cost of 6.25% addition. Compared with the current optimized RNN OMS and approximate BP(RNN-OMS-BP) decoding algorithm, the decoding algorithm in this paper uses improved information to reduce 25% of the addition operations with almost no loss in BER performance, while saving part of the storage space overhead. Under the same BER performance, it reduces the number of iterations by 37.5%.

Akira Yamada, Tomoaki Otsuki Ohtsuki