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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Performance evaluation of cooperative mobile communication security using reinforcement learning.

Gebrehiwet Gebrekrstos Lema¹, Kiros Siyoum Weldemichael², Leake Enqay Weldemariam³

¹School of Electrical and Computer Engineering, Mekelle University, Ethiopia.

|June 7, 2021

Summary

This study enhances wireless network security using reinforcement learning for cooperative communication. The proposed method improves secrecy capacity against eavesdroppers in mobile networks.

Keywords:

Energy efficiency Mobile network security Reinforcement learning

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Area of Science:

Wireless Communication Networks
Network Security
Information Theory

Background:

Wireless networks face impairments like shadowing, path loss, and fading, degrading signal quality.
Cooperative communication offers solutions via alternate routes and spatial diversity, improving channel stability.
Cellular network advancements introduce new security challenges, particularly the vulnerability of cooperative communications to eavesdroppers.

Purpose of the Study:

To increase the secrecy capacity of wireless networks with moving cooperative devices.
To address security vulnerabilities in cooperative wireless mobile networks.

Main Methods:

A reinforcement learning technique was developed to optimize transmit parameters.
The system learns optimal parameters through interaction between transmitter, receiver, relay, and eavesdropper.

Main Results:

The proposed reinforcement learning technique significantly enhanced the secrecy level for legitimate receivers.
Demonstrated improved security in cooperative wireless networks.

Conclusions:

Reinforcement learning is an effective method for enhancing security in cooperative wireless networks.
The research provides a viable solution for secure mobile communication in the presence of eavesdropping.