Intelligent Reflecting Surface-Assisted Secure Multi-Input Single-Output Cognitive Radio Transmission

Area of Science:

• Wireless Communications
• Information Security
• Signal Processing

Background:

• Intelligent Reflecting Surfaces (IRS) offer low-complexity, energy-efficient solutions for future wireless networks.
• Cognitive Radio (CR) networks face security challenges that require advanced solutions.
• Physical Layer Security (PLS) is crucial for securing wireless communications.

Purpose of the Study:

• To investigate the integration of IRS with PLS to enhance the security of CR networks.
• To maximize the secrecy rate of secondary users in an IRS-assisted MISO CR wiretap channel.
• To address power and interference constraints in the CR network.

Main Methods:

• An alternating optimization (AO) algorithm was developed to jointly optimize transmit covariance (R) and IRS phase shifts (Q).
• Quasi-convex sub-problems were transformed into convex ones for global optimization of R.
• Bisection search and a minorization-maximization (MM) algorithm were used to optimize Q, with a nested bisection search for global solutions.

Main Results:

• The proposed AO algorithm effectively optimizes R and Q, leading to enhanced secrecy rates.
• The IRS-assisted design significantly improves the secondary user's secrecy rate compared to non-IRS methods.
• The system demonstrates robust performance, with secrecy rate increasing with transmit power even with active interference constraints.

Conclusions:

• IRS technology is highly effective in bolstering the physical layer security of CR networks.
• The proposed optimization algorithm provides a viable solution for maximizing secrecy rates under practical constraints.
• The IRS-assisted approach offers a promising direction for secure and efficient beyond 5G/6G wireless systems.