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Robust Optimization for IRS-Assisted SAGIN Under Channel Uncertainty

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  • Xu Zhu

    (School of Computer Science, Central South University, Changsha 410083, China
    School of Information Engineering, Hunan Industrial Vocational and Technical College, Hengyang 410208, China)

  • Litian Kang

    (School of Computer Science, Central South University, Changsha 410083, China)

  • Ming Zhao

    (School of Computer Science, Central South University, Changsha 410083, China)

Abstract

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments.

Suggested Citation

  • Xu Zhu & Litian Kang & Ming Zhao, 2025. "Robust Optimization for IRS-Assisted SAGIN Under Channel Uncertainty," Future Internet, MDPI, vol. 17(10), pages 1-19, October.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:10:p:452-:d:1762697
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