… Motivated by these facts, a sphere decoding algorithm based on DL is proposed in this paper,
where the radius of the decoding hypersphere is learned by a DNN prior to decoding. The …

… Abstract—Although the sphere decoder (SD) is a powerful detector for multiple-input … To
overcome this challenge, we propose fast deep learning (DL)aided SD (FDL-SD) and fast DL-…

… learning-aided sphere decoding (SD) scheme for large multiple-input–multiple-output systems,
namely, deep path prediction-based sphere decoding … metrics of the ‘‘deep’’ paths in sub-…

… Abstract—In this paper, we propose a deep learning based sphere decoding (SD) scheme
… Specifically, we first design the sparsely connected deep neural network (SC-DNN) to find a …

… To the best of our knowledge, there is only one work in the litterature that describes a deep
learning based SD [23]. The aim of that work is to learn the minimum radius that give the …

… We compare our results with the soft sphere decoder [26]. … In order to quantify the performance
of our deep learning decoder … is very close to the soft sphere decoder, when the BER and …

… been published on this topic proving that the application of deep learning to MIMO decoding
can drastically reduce the complexity of the decoder while preserving its performance [23]–[…

… Sphere Decoders offer much higher detection accuracy than simple linear equalizers, at
the cost of much more expensive accelerators. Some cost increase is unavoidable since …

… In this study, we proposed a general deep learning framework for decoding and mapping
ongoing brain task states from whole-brain fMRI signals of humans. After training and testing it …

… In this paper, a low-complexity detection algorithm inspired by sphere decoding is … -bit-sphere-decoding
(OSD). The key idea of the OSD is to perform the proposed MWD over a sphere, …