Robust Optimization for IRS-Assisted SAGIN Under Channel Uncertainty

Xu Zhu, Litian Kang and Ming Zhao ()
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Xu Zhu: School of Computer Science, Central South University, Changsha 410083, 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

Future Internet, 2025, vol. 17, issue 10, 1-19

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.

Keywords: SAGIN; IRS; wireless communication (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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