… 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-…

… Second, we propose a DL-based list sphere decoding (DLLSD) algorithm that selects and
updates the initial radius of the original LSD to guarantee a pre-defined number NL of lattice …

… We present analytical algorithms that further reduce the decoding latency of the low complexity
SD-SDS. We propose a deep learning-based early stopping algorithm that prematurely …

… 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 …

… 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 …

… been proposed, eg, MIMO detectors based on sphere decoding [13]–[15], … decoders that
require soft input to achieve superior decoding performance. Sphere decoding and list decoding …

… 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 …