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A Novel Starfish Optimization Algorithm for Secure STAR-RIS Communications.

Mona Gafar1, Shahenda Sarhan2,3, Abdullah M Shaheen4

  • 1Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.

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Summary
This summary is machine-generated.

The Enhanced Starfish Optimization (ESFO) algorithm improves secure wireless communication by optimizing Simultaneously Transmitting and Reflecting RIS (STAR-RIS) systems. ESFO offers superior performance and faster convergence for next-generation networks.

Keywords:
achievable rate limitationreconfigurable intelligent surfacessimultaneously transmitting and reflectingstarfish optimizer

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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.