Enhanced MIMO-SCMA Detector by Deep Learning
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Abstract
In this paper, we propose a deep learning approach for multiple input multiple output - sparse code multiple access (MIMO-SCMA) signal detection by using a deep neural network via spreading the procedure of the message passing algorithm (MPA). The MPA can be transformed into a sparsely connected neural network. The neural network can be trained off-line and then implemented for online detection. Besides when the neural network has been trained, the network weights corresponding to the edges of a factor graph. From the simulation result of the MIMO-SCMA system over the quasi-static Rayleigh fading channel, found that the neural network detection better performance than the traditional MPA.
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