Optimizing Reconfigurable Intelligent Surfaces for Improved Space-based Communication Amidst Phase Shift Errors

TL;DR

This paper develops an efficient optimization method for reconfigurable intelligent surfaces in satellite communication, accounting for phase shift errors using a stochastic framework and advanced algorithms to improve system performance.

Contribution

It introduces a novel simplified optimization approach for ideal conditions and extends it to handle phase errors with a Monte Carlo-based stochastic framework and BFGS algorithm.

Findings

Optimized phase shifts improve received power in RIS-assisted satellite links.

Phase errors significantly impact system performance, and the proposed method effectively mitigates this.

The framework provides practical insights for real-world satellite communication systems.

Abstract

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for enhancing satellite communication systems by manipulating the phase of electromagnetic waves. This study addresses optimising phase shift values (\phi_{R}) in RIS networks under both ideal and non-ideal conditions. For ideal scenarios, we introduce a novel approach that simplifies the traditional optimisation methods for determining the optimal value. Leveraging trigonometric identities and the law of cosines, we create a more tractable formulation for the received power that allows for efficient optimisation of \phi_{R}. However, practical applications often grapple with non-ideal conditions. These conditions can introduce phase errors, significantly affecting the received signal and overall system performance. To accommodate these complexities, our optimisation framework extends to include phase errors, which are modelled as a uniform distribution. To solve this optimisation problem, we propose a stochastic framework that harnesses the Monte Carlo method to consider all plausible phase error values. Furthermore, we employ the Broyden Fletcher Goldfarb Shanno (BFGS) algorithm, an iterative method known for its efficacy. This algorithm systematically updates \phi_{R} values, incorporating the gradient of the objective function and Hessian matrix approximations. The algorithm also monitors convergence to balance computational complexity and accuracy. The results of the theoretical analysis are illustrated with several examples. As herein demonstrated, the proposed solution offers profound insights into the impacts of phase errors on RIS system performance. It also unveils innovative optimisation strategies for real-world satellite communication scenarios under diverse conditions.