Reciprocity Calibration of Dual-Antenna Repeaters via MMSE Estimation

TL;DR

This paper introduces a Bayesian MMSE-based reciprocity calibration method for MIMO systems with repeaters, improving accuracy and convergence speed while maintaining low complexity.

Contribution

It presents a novel Bayesian MMSE calibration algorithm that incorporates statistical knowledge and a von Mises denoiser for enhanced phase alignment in MIMO repeater systems.

Findings

Significantly improved estimation accuracy over NLS methods

Fast convergence suitable for practical deployment

Maintains low computational complexity

Abstract

This paper proposes a novel Bayesian reciprocity calibration method that consistently ensures uplink and downlink channel reciprocity in repeater-assisted multiple-input multiple-output (MIMO) systems. The proposed algorithm is formulated under the minimum mean-square error (MMSE) criterion. Its Bayesian framework incorporates complete statistical knowledge of the signal model, noise, and prior distributions, enabling a coherent design that achieves both low computational complexity and high calibration accuracy. To further enhance phase alignment accuracy, which is critical for calibration tasks, we develop a von Mises denoiser that exploits the fact that the target parameters lie on the circle in the complex plane. Simulation results demonstrate that the proposed MMSE algorithm achieves substantially improved estimation accuracy compared with conventional deterministic non-linear least-squares (NLS) methods, while maintaining comparable computational complexity. Furthermore, the proposed method exhibits remarkably fast convergence, making it well suited for practical implementation.