A Novel Starfish Optimization Algorithm for Secure STAR-RIS Communications

Area of Science:

• Wireless Communication
• Optimization Algorithms
• Physical Layer Security

Background:

• Next-generation wireless networks require enhanced security and efficiency.
• Reconfigurable Intelligent Surfaces (RIS) are emerging technologies for wireless communication.
• Physical layer security is crucial for protecting sensitive data transmission.

Purpose of the Study:

• To develop an Enhanced Starfish Optimization (ESFO) algorithm for secure wireless communication.
• To optimize the performance of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) systems.
• To enhance the physical layer security of wireless networks.

Main Methods:

• Developed an Enhanced Starfish Optimization (ESFO) algorithm inspired by starfish behavior.
• Integrated a fitness-based interacting mechanism into the ESFO algorithm to improve exploitation.
• Designed a secure beamforming architecture optimizing Base Station (BS) precoding and STAR-RIS coefficients.
• Compared ESFO with benchmark optimizers (SFO, DO, NNA, CAOA, WSO) across various transmit power thresholds.

Main Results:

• The proposed ESFO algorithm consistently outperformed existing benchmark approaches.
• ESFO demonstrated improved performance (20-25%) and faster convergence (40-50%) compared to traditional algorithms.
• The ESFO-enabled STAR-RIS architecture achieved superior secrecy capacity over standard RIS-based systems.
• Low-resolution STAR-RIS phase-shifters were found sufficient for robust secrecy performance.

Conclusions:

• The Enhanced Starfish Optimization (ESFO) algorithm is effective for optimizing secure wireless communication infrastructures.
• ESFO provides a robust and efficient solution for STAR-RIS-assisted secure communication systems.
• This research contributes to the advancement of intelligent communication infrastructures with enhanced security and performance.