Reciprocity Calibration of Dual-Antenna Repeaters via MMSE Estimation

2026-02-05

AI Summary

This work addresses the challenging problem of reciprocity calibration in relay-assisted MIMO systems by proposing the first calibration method that integrates Bayesian inference with the circular nature of phase parameters. Building upon the minimum mean square error (MMSE) criterion, the authors develop a comprehensive Bayesian framework that jointly incorporates the signal model, noise statistics, and prior information. A key innovation is the introduction of a von Mises denoiser, specifically designed to handle phase parameters residing on the unit circle in the complex plane, thereby enabling high-precision phase alignment. Compared to conventional nonlinear least squares approaches, the proposed method achieves significantly improved calibration accuracy and faster convergence while maintaining comparable computational complexity, making it well-suited for practical deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• reciprocity calibration
• dual-antenna repeaters
• MIMO systems
• phase alignment
• channel reciprocity

Innovation

• MMSE estimation
• Bayesian reciprocity calibration
• von Mises denoiser
• MIMO repeaters
• channel reciprocity