Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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

Active Publication Date: 2019-11-19
ANHUI UNIVERSITY
View PDF3 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for establishing SCMA codec model based on noise reduction auto-encoder
  • Method for establishing SCMA codec model based on noise reduction auto-encoder
  • Method for establishing SCMA codec model based on noise reduction auto-encoder

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[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,...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a method for establishing an SCMA codec model based on a noise reduction auto-encoder, and the method comprises the steps: building the SCMA encoder based on the noise reduction auto-encoder and a full-connection neural network, and enabling the original input data of a user to be mapped into a code word; superposing and transmitting the code words of all the users on eachresource block, and superposing channel noise on the signal on each resource block; establishing an SCMA decoder based on a full-connection neural network at a receiving end, and decoding original input data of all users; training an SCMA codec model based on a noise reduction auto-encoder; and testing the BER performance of the SCMA codec model based on the noise reduction auto-encoder. Comparedwith a traditional SCMA system, the method has the advantages that the encoding and decoding complexity is reduced; compared with an existing SCMA system model based on deep learning, the method hasthe advantages that the bit error rate is further reduced; compared with the existing SCMA system model based on deep learning, the SCMA system model based on deep learning has a 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,...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): H04L1/00H04L25/03G06N3/08G06N3/04
CPCH04L1/0041H04L1/0048H04L1/005H04L25/03165G06N3/08G06N3/045
Inventor 胡艳军胡梦钰蒋芳王翊许耀华
Owner ANHUI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products