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Communication behavior recognition using CNN-based signal analysis.

Hao Meng¹, Yingke Lei¹, Fei Teng¹

¹School of Electronic Countermeasures, National University of Defense Technology, Anhui, China.

Peerj. Computer Science

|June 10, 2024

Summary

This summary is machine-generated.

This study introduces a new system for real-time recognition of non-cooperative communication behavior using a convolutional neural network (CNN). The CNN effectively segments data, enabling accurate identification of communication activities even with interference.

Keywords:

Actual environmental adaptability Communication behavior cognition Convolutional neural network Deep learning Non cooperative communication behavior cognition

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Area of Science:

Signal processing
Machine learning
Communication engineering

Background:

Traditional signal analysis for non-cooperative communication behavior lacks real-time capabilities.
Accurate identification of communication station signals is crucial for various applications.

Purpose of the Study:

To develop a real-time system for recognizing non-cooperative communication behavior.
To evaluate the performance of a one-dimensional convolutional neural network (CNN) in this task.

Main Methods:

A pragmatic architecture for communication behavior recognition was designed.
A polling-based system incorporating a one-dimensional CNN was implemented for data segmentation.
The CNN's reliability was tested against noise, varying signal lengths, frequency interferences, and dynamic location changes.

Main Results:

The one-dimensional CNN demonstrated effective data segmentation for recognizing communication activities.
The system achieved reliable performance across diverse real-world scenarios, including noisy environments and dynamic conditions.
Experimental results confirmed the efficacy and dependability of the CNN approach.

Conclusions:

The developed CNN-based system offers a viable solution for real-time recognition of non-cooperative communication behavior.
The approach shows robustness and accuracy in challenging signal analysis contexts.
This technology enhances the ability to monitor and understand communication activities in dynamic environments.