Method for establishing SCMA codec model based on noise reduction auto-encoder
A self-encoder and codec technology, which is applied in the establishment of SCMA codec model, can solve the problems of slow convergence speed and long training time of deep neural network
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[0113] Take the case of 6 users and 4 resource blocks as an example.
[0114] Such as figure 1 As shown, based on the idea of denoising self-encoder, a certain proportion of random noise is added to the input data, and the SCMA encoder maps the user's original input data into codewords; the encoded codewords are superimposed on the resource blocks in a non-orthogonal manner Transmission, affected by the channel during transmission, the signal is superimposed with noise; a SCMA decoder based on a fully connected neural network is established at the receiving end, and the original input information of all users is decoded according to the input codeword; the above SCMA codec can As a complete noise reduction autoencoder structure, superimpose a fixed size channel noise to train the above SCMA codec model based on noise reduction autoencoder; test the BER of the above SCMA codec model based on noise reduction autoencoder performance. Compared with the traditional SCMA system,...
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