[Paper Review] Large-scale IRS-aided MIMO over Double-scattering Channel: An Asymptotic Approach.

Xin Zhang, Xianghao Yu+2 more|arXiv (Cornell University)|Apr 13, 2021

Advanced Wireless Communication Technologies12 references4 citations

TL;DR

This paper proposes a deterministic approximation (DA) of the ergodic rate in large-scale IRS-aided MIMO systems with statistical CSI, leveraging random matrix theory to enable low-complexity optimization. The DA is proven to be tight and unique, and an alternating optimization algorithm achieves significant ergodic rate gains by jointly optimizing phase shifts and signal covariance matrices.

ABSTRACT

Intelligent reflecting surface (IRS) is a promising enabler for next-generation wireless communications due to its reconfigurability and high energy efficiency in improving the propagation condition of channels. In this paper, we consider a large-scale IRS-aided multiple-input-multiple-output (MIMO) communication system in which statistical channel state information (CSI) is available at the transmitter. By leveraging random matrix theory, we first derive a deterministic approximation (DA) of the ergodic rate with low computation complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm to obtain a locally optimal solution for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can improve the ergodic rate effectively.

Motivation & Objective

• To address the challenge of optimizing ergodic rate in large-scale IRS-aided MIMO systems under statistical CSI at the transmitter.
• To develop a low-complexity deterministic approximation (DA) of the ergodic rate that is both accurate and analytically tractable.
• To prove the existence and uniqueness of the DA parameters for reliable performance prediction.
• To design an alternating optimization algorithm that jointly optimizes phase shifts and signal covariance matrices to maximize the DA of the ergodic rate.
• To validate the tightness of the DA and the effectiveness of the proposed optimization in improving system ergodic rate.

Proposed method

• Leverages random matrix theory to derive a deterministic approximation (DA) of the ergodic rate, replacing the stochastic rate with a deterministic equivalent for tractable optimization.
• Proves the existence and uniqueness of the DA parameters under the large-system regime, ensuring analytical stability and convergence.
• Proposes an alternating optimization algorithm that iteratively updates phase shifts and signal covariance matrices to maximize the DA of the ergodic rate.
• Uses the DA as a surrogate objective function, enabling low-complexity optimization without requiring instantaneous CSI.
• Employs asymptotic analysis to model the double-scattering channel in large-scale IRS-MIMO systems, capturing statistical channel behavior.

Experimental results

Research questions

• RQ1 Can a deterministic approximation of the ergodic rate be derived for large-scale IRS-aided MIMO systems with statistical CSI, and is it tight in practical scenarios?
• RQ2 What are the conditions under which the deterministic approximation parameters exist and are unique?
• RQ3 How can the ergodic rate be maximized efficiently when only statistical CSI is available at the transmitter?
• RQ4 To what extent does the proposed alternating optimization algorithm improve the ergodic rate compared to baseline schemes?
• RQ5 How does the proposed DA compare in accuracy and complexity to conventional instantaneous CSI-based methods?

Key findings

• The deterministic approximation (DA) of the ergodic rate is proven to be both existent and unique under the large-system regime, ensuring analytical robustness.
• The DA is numerically shown to be tight, meaning it closely approximates the true ergodic rate with minimal deviation.
• The proposed alternating optimization algorithm effectively maximizes the DA of the ergodic rate by jointly optimizing phase shifts and signal covariance matrices.
• Numerical results demonstrate that the proposed method achieves significant ergodic rate gains over conventional schemes without requiring instantaneous CSI.
• The method achieves low computation complexity due to the use of the deterministic approximation, making it suitable for large-scale IRS-aided MIMO deployments.