RIS-aided MIMO Beamforming: Piece-Wise Near-field Channel Model

TL;DR

This paper introduces a piece-wise near-field channel model for RIS-aided MIMO systems, improving spectral efficiency and robustness against channel estimation errors compared to traditional models.

Contribution

It proposes a novel piece-wise near-field channel model that balances modeling accuracy and robustness, along with a joint beamforming design algorithm for enhanced system performance.

Findings

Piece-wise near-field model outperforms traditional models in spectral efficiency.

The proposed approach offers a good trade-off between accuracy and degrees of freedom.

Simulation confirms improved robustness against channel estimation errors.

Abstract

This paper proposes a joint active and passive beamforming design for reconfigurable intelligent surface (RIS)-aided wireless communication systems, adopting a piece-wise near-field channel model. While a traditional near-field channel model, applied without any approximations, offers higher modeling accuracy than a far-field model, it renders the system design more sensitive to channel estimation errors (CEEs). As a remedy, we propose to adopt a piece-wise near-field channel model that leverages the advantages of the near-field approach while enhancing its robustness against CEEs. Our study analyzes the impact of different channel models, including the traditional near-field, the proposed piece-wise near-field and far-field channel models, on the interference distribution caused by CEEs and model mismatches. Subsequently, by treating the interference as noise, we formulate a joint active and passive beamforming design problem to maximize the spectral efficiency (SE). The formulated problem is then recast as a mean squared error (MSE) minimization problem and a suboptimal algorithm is developed to iteratively update the active and passive beamforming strategies. Simulation results demonstrate that adopting the piece-wise near-field channel model leads to an improved SE compared to both the near-field and far-field models in the presence of CEEs. Furthermore, the proposed piece-wise near-field model achieves a good trade-off between modeling accuracy and system's degrees of freedom (DoF).