Method of establishing scma codec model based on denoising autoencoder

Abstract

The invention relates to a method for establishing a SCMA codec model based on a noise reduction autoencoder, comprising: establishing an SCMA encoder based on a noise reduction autoencoder and a fully connected neural network, and mapping the user's original input data into a codeword ; Superimpose and transmit the codewords of all users on each resource block, and then superimpose the channel noise on the signal on each resource block; establish a SCMA decoder based on a fully connected neural network at the receiving end to decode the original input of all users Data; train the denoising autoencoder-based SCMA codec model; test the BER performance of the above denoising autoencoder-based SCMA codec model. Compared with the traditional SCMA system, the present invention reduces the complexity of encoding and decoding; compared with the existing SCMA system model based on deep learning, the present invention further reduces the bit error rate; compared with the existing deep learning-based SCMA system model, the invention has faster training convergence speed.

Description

technical field [0001] The invention relates to the technical fields of wireless communication and deep learning, in particular to a method for establishing a SCMA codec model based on a noise reduction autoencoder. Background technique [0002] Sparse code multiple access (SCMA) is a non-orthogonal multiple access method that can provide high spectral efficiency and large-scale connectivity that meet the requirements of 5G wireless communication systems. The encoding end and decoding end of the SCMA system constitute the main components of the system. Each user has its own dedicated codebook, and the encoded bits are directly mapped to a multi-dimensional codeword in the SCMA codebook, and non-orthogonal superposition is performed on the resources. Taking advantage of the sparseness of user codewords in the SCMA system, multi-user Detection, while the algorithm used in the current multi-user detection scheme is mainly the Message Passing Algorithm (MPA). [0003] However,...

AI Technical Summary

Problems solved by technology

But there are still problems: 1) Although the bit error rate of the D-SCMA system is lower than that of the traditional SCMA system, it is because the neural network has also learned a better codeword mapping, but its decoding performance is still inferior to that of the traditional SCMA system. MPA algorithm; 2) The slow convergence speed of the deep neural network used in the D-SCMA system results in longer training time

Images