… channel decoding targeting short and moderate length codes. In this paper, we consider the
Syndrome-based Deep Learning Decoder … no correlations between the transmitted symbols. …

… Reducing latency of decoding has always been a tricky task. Recently, deep learning (DL) …
, each with 125 data symbols, we can transmit 20 × 125 = 2, 500 data symbols in each radio …

… We assume that prior probabilities of transmitted symbols are equal. Table I shows the … a
substantial opportunity where deep learning techniques are applied in communication system. …

… For example, a methodology is proposed in [18] for learning symbol … deep learning in this
scenario is that the labeling, the positions of the constellation points as well as the decoder …

… This section describes the implementation method of deep learning structure for … symbols
are considered as one-hot encoding vector with length of MT which is the possible symbol …

… issues will be addressed by deep learning (DL) in this work. … and CDMA to spread data
symbols of multiple users over the … For the signal decoding, we assume that the CSI h is perfectly …

… For decoding, we adopted a multilabel classification architecture with … data symbols to be
estimated. While feasible for high MIMO dimensions, the adopted DL architecture for decoding …

… intended symbol to be i. Finally, receiver k declares ˆmk, which corresponds to the index of
the element of pk with the highest probability, as the decoded index of the intended symbol. …

… Deep learning is an approach to machine learning that involves training neural networks with
… The model introduced in this paper (SCAN) is able to associate new symbols with learned …

… To this effect, we apply deep learning to enhance the luminescence imaging resolution of
lanthanide-doped NCs, achieving successful information storage and readout through pork …