Offset min-sum Optimization for General Decoding Scheduling: A Deep Learning Approach - Abotabl, Ahmed; Bae, Jung Hyun; Song, Kee-Bong (IEEE Vehicular Technology Conference, 2019)
Deep learning has shown an unprecedented success in many fields such as computer vision and speech recognition, providing solutions to intractable problems. Deep learning has also provided solutions to several intractable problems in communications systems. This paper uses a deep learning approach to optimize the analytically intractable offset value in the offset-min-sum (OMS) algorithm. OMS algorithm is a very attractive low complexity algorithm that is used in the belief propagation decoding of linear codes. The contributions of this paper are: First, providing a low complexity offset optimization framework based on gradient descent and back propagation on the original Tanner graph. Our proposed algorithm has comparable complexity and similar operation as the forward belief propagation decoding algorithm and hence, can be trained much more efficiently. Second, the framework can be easily extended to any decoding scheduling such as flooding or layered scheduling. Training results show that the proposed framework can find the optimal offset value under different decoding scheduling with the same complexity of the belief propagation algorithm.