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Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to

Xuerong Wang¹, Shanshan Rao², Liang Zhang³

¹China Telecom Research Institute, Guangzhou, 510630, Guangdong, China.

Scientific Reports

|October 10, 2024

Summary

This summary is machine-generated.

This study introduces a parallel Convolutional Neural Network (CNN) for resource allocation in Internet of Things (IoT) systems. The method enhances secure communication rates for cellular users (CUs) in embedded systems.

Keywords:

Communication security D2D Embedded system Multi-scale parallel CNN

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Area of Science:

Computer Science
Electrical Engineering
Telecommunications

Background:

Internet of Things (IoT) services drive demand for efficient multimedia communication in embedded systems.
Existing systems face challenges with low spectral efficiency and network flexibility.
Device-to-device (D2D) users reusing cellular user (CU) spectrum cause interference.

Purpose of the Study:

To propose a resource allocation scheme for optimizing multimedia communication in embedded systems.
To enhance spectral efficiency and network flexibility.
To maximize secure transmission rates for CUs while ensuring D2D communication security.

Main Methods:

A parallel Convolutional Neural Network (CNN) based resource allocation scheme is proposed.
Optimization of base station beamforming vectors and Reconfigurable Intelligent Surface (RIS) phase shifts.
Incorporation of multi-scale residual modules within CNN convolutional layers.

Main Results:

The proposed scheme effectively mitigates interference between D2D and CU users.
Significant improvements in system secrecy rates were observed.
The CNN-based model demonstrated superior performance compared to traditional methods in simulations.

Conclusions:

The parallel CNN-based resource allocation scheme enhances the security and efficiency of embedded system communications.
The approach ensures secure multimedia computing.
This method offers a promising solution for future IoT communication networks.