A sparse code division multiple access signal detection method based on depth neural network

A deep neural network and sparse code division multiple access technology, applied in the field of multi-user detection, can solve problems such as high computational complexity and FPGA resource consumption, and achieve the effect of reducing computational pressure, principles and implementation methods.

Active Publication Date: 2018-12-18
SOUTHEAST UNIV
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AI Technical Summary

Benefits of technology

This patented technology improves upon previous methods for detecting objects such as people or things quickly without overwhelming their CPUs. It also reduces processing time compared to older techniques while still being effective at identifying small targets accurately. Overall, it provides technical benefits like improved accuracy and efficiency in object recognition systems.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving wireless network performance while reducing power consumption without sacrificing bandwidth usage or requiring expensive equipment such as deep learning algorithms.

Method used

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  • A sparse code division multiple access signal detection method based on depth neural network
  • A sparse code division multiple access signal detection method based on depth neural network
  • A sparse code division multiple access signal detection method based on depth neural network

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048]The complex signals of multiple users are superimposed on multiple resource blocks, and the number of users is greater than the number of resources. At this time, information of multiple users is carried on one resource block. The traditional solution is to use the belief propagation algorithm to iterate the information on the edges of the factor graph, and the algorithm converges after several iterations. However, the MPA algorithm based on this principle has high computational complexity and consumes a lot of FPGA resources. To this end, this method provides a SCMA detection algorithm based on a deep neural network, which transfers the consumption of computing power to the currently idle AI chip, and through parameter training, this method also obtains a small amount of performance improvement. This method specifically comprises the following steps:

[0049] (...

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Abstract

The invention discloses a sparse code division multiple access signal detection method based on a depth neural network. First, the relationship between users and resources is expressed as factor graph, Then the iterative transfer of information on the factor graph is transformed into the forward transfer of neural network, and then the signal detection error is estimated and the estimated value isused as the input data of the neural network, and then the gradient descent method is used to train the neural network to obtain better coefficients, and finally the trained network is used for SCMAsignal detection. Because of the use of neural network architecture, this method can be implemented on the corresponding AI chip. Compared with the traditional methods, this method can improve the performance and transfer the related computations to the high-speed parallel processing AI chip, which can effectively reduce the time delay caused by SCMA signal detection.

Description

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Claims

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Application Information

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Owner SOUTHEAST UNIV
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