14
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      An Enhanced SCMA Detector Enabled by Deep Neural Network

      Preprint
      , , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a computationally efficient since highly paralleled computations in the network are enabled in emerging Artificial Intelligence (AI) chips.

          Related collections

          Most cited references2

          • Record: found
          • Abstract: not found
          • Article: not found

          Factor graphs and the sum-product algorithm

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Learning to Decode Linear Codes Using Deep Learning

            A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained using deep learning techniques. A well-known property of the belief propagation algorithm is the independence of the performance on the transmitted codeword. A crucial property of our new method is that our decoder preserved this property. Furthermore, this property allows us to learn only a single codeword instead of exponential number of code-words. Improvements over the belief propagation algorithm are demonstrated for various high density parity check codes.
              Bookmark

              Author and article information

              Journal
              24 August 2018
              Article
              1808.08015
              b6ac1774-6fae-4e07-b095-b7a15b019d84

              http://creativecommons.org/publicdomain/zero/1.0/

              History
              Custom metadata
              cs.IT math.IT

              Numerical methods,Information systems & theory
              Numerical methods, Information systems & theory

              Comments

              Comment on this article