Secure channel estimation model for cognitive radio network physical layer security using two-level shared key...

Area of Science:

• Wireless Communication
• Network Security
• Signal Processing

Background:

• Physical Layer Security (PLS) is crucial for confidentiality, availability, and integrity in Cognitive Radio Networks (CRN).
• Existing security models for sensing and beamforming in CRNs aim to mitigate eavesdropper impact.
• Effective security mechanisms are needed to counter adversaries in dynamic CRN environments.

Purpose of the Study:

• To introduce a Secure Channel Estimation Model (SCEM) to enhance PLS in CRNs.
• To leverage Channel State Information (CSI) and Deep Learning (DL) for improved security and channel utilization.
• To ensure secure communication and authentication between primary and secondary users.

Main Methods:

• Developed a Secure Channel Estimation Model (SCEM) integrating CSI and DL.
• Utilized CSI to assess channel utilization and capacity.
• Implemented a 2-level key shared authentication with DL-based verification for secure communication.
• Monitored outage secrecy rates to manage allocation failures.

Main Results:

• The proposed SCEM model improved the secrecy rate by 10.77%.
• The probability of detection was reduced by 15.01%.
• Interference rate decreased by 11.07% across varying transmit powers.

Conclusions:

• The SCEM model effectively enhances PLS in CRNs by maximizing CSI exploitation and beamforming.
• DL-based authentication ensures secure and efficient channel capacity utilization.
• The model provides robust security against adversaries, improving overall network performance.