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

• Published in: Journal of signal processing systems Vol. 92; no. 10; pp. 1045 - 1056
• Main Authors: Lyu, Ning, Dai, Bin, Wang, Hongfei, Yan, Zhiyuan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Circuits and Systems; Cluster analysis; Clustering; Computer architecture; Computer Imaging; Computer simulation; Decoders; Decoding; Electrical Engineering; Energy consumption; Engineering; Field programmable gate arrays; Hardware; Image Processing and Computer Vision; Machine learning; Mathematical models; Message passing; Neural networks; Optimization; Parameters; Pattern Recognition; Pattern Recognition and Graphics; Performance degradation; Scaling factors; Signal,Image and Speech Processing; Vector quantization; Vision; Deep learning; Hardware implementation; Belief Propagation (BP); Polar codes
• ISSN: 1939-8018; 1939-8115
• DOI: 10.1007/s11265-020-01561-y

Polar codes have received a lot of attention due to their capacity-achieving performance and low complexity. This paper proposes a novel scaling offset min-sum (SOMS) algorithm and adapts the offset min-sum (OMS) algorithm for polar codes, and both algorithms are improved via learning. For all message updates, conventional min-sum decoding algorithms use the same scaling factor or offset, which is usually obtained by numerical simulations. By modeling the data flow of min-sum algorithms as a deep neural network, the parameters used in the message passing updates of min-sum decoders can be different for each message update, and are obtained by training and optimizing the corresponding deep neural network. The simulation results show that the proposed SOMS algorithm based on deep learning performs better than all existing BP-based algorithms. Moreover, this paper presents an efficient hardware architecture of the proposed SOMS algorithm. The K-Means clustering algorithm is applied to reduce the number of possible parameters of the neural network, leading to reduced energy consumption and memory requirement with negligible error performance degradation. The proposed architecture of the SOMS algorithm for a (256,128) polar code is implemented and validated on the Xilinx Artix-7 field-programmable gate array.