We revisit the idea of using deep neural networks for one-shot decoding of random and
structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (…

… a deep-learning approach for the joint MIMO detection and channel decoding problem. …
model-based approach for MIMO detection and channel decoding in linear or iterative manners. …

… to learn than random codes, and addressed the challenge that deep learning based decoders
… the decoder for each sub-codeword being trained separately. Although the combination of …

… Contrary to many other deep learning-based decoding approaches, the … decoder against
state-of-the-art in conventional channel decoding as well as against recent deep learning-…

… Motivated by these facts, a sphere decoding algorithm based on DL is … decoding hypersphere
is learned by a DNN prior to decoding. The DNN maps a sequence of the fading channel …

… channel decoding methods, there is no prior information used in deep learning methods…
However, the experiment results show that the deep learning based channel decoders have …

… an application of deep learning methods to the problem of low complexity channel decoding.
A … near Shannon channel capacity with the belief propagation (BP) decoding algorithm, but …

… in applying deep learning to wireless communications. Deep learning based channel decoder
… It is shown that belief propagation decoders for LDPC and Polar codes can be imitated by …

… learning decoders, a natural question is whether we can learn both the encoder and the
decoder via deep learning … We model both the encoder and the decoder as neural networks, as …

… that one way to reach scalability of deep-learning based channel decoding can be
described by replacing sub-components of an existing decoder by NN-based components. This …