Application of Tensor Decomposition to Reduce the Complexity of Neural Min-Sum Channel Decoding Algorithm - Qingle Wu; Benjamin K. Ng; Yuanhui Liang; Chan-Tong Lam; Yan Ma (Applied Sciences, 2023)
Channel neural decoding is very promising as it outperforms the traditional channel decoding algorithms. Unfortunately, it still faces the disadvantage of high computational complexity and storage complexity compared with the traditional decoding algorithms. In this paper, we propose that low rank decomposition techniques based on tensor train decomposition and tensor ring decomposition can be utilized in neural offset min-sum (NOMS) and neural scale min-sim (NSMS) decoding algorithms. The experiment results show that the proposed two algorithms achieve near state-of-the-art performance with low complexity.