Deep Learning Based Sphere Decoding

arXiv:1807.03162v2 Announce Type: replace-cross
Abstract: In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to DNN's ability of intelligently learning …

abstract algorithm arxiv cs.lg decoding decoding algorithm deep learning deep neural network dnn eess.sp likelihood network neural network noise paper performance signal sphere type