Recursive Flow: A Generative Framework for MIMO Channel Estimation

2026-01-22

AI Summary

This work addresses the challenge of accurately estimating channel state information in massive MIMO systems from noisy, underdetermined measurements by proposing a Recursive Flow (RC-Flow) framework. RC-Flow integrates flow-matching priors with a data fidelity term to construct a closed-loop recursive optimization mechanism, featuring a novel serial restart and anchored trajectory correction architecture. An adaptive dual-scheduling strategy is introduced to balance convergence speed and estimation accuracy. Theoretical analysis establishes the global asymptotic stability of the recursive operator. Experimental results demonstrate that RC-Flow achieves a 2.7 dB performance gain over score-based baselines under low signal-to-noise ratios while reducing inference latency by two orders of magnitude.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In this work, we introduce Recursive Flow (RC-Flow), a novel solver that leverages pre-trained flow matching priors to robustly recover channel state information from noisy, under-determined measurements. Different from conventional open-loop generative models, our approach establishes a closed-loop refinement framework via a serial restart mechanism and anchored trajectory rectification. By synergizing flow-consistent prior directions with data-fidelity proximal projections, the proposed RC-Flow achieves robust channel reconstruction and delivers state-of-the-art performance across diverse noise levels, particularly in noise-dominated scenarios. The framework is further augmented by an adaptive dual-scheduling strategy, offering flexible management of the trade-off between convergence speed and reconstruction accuracy. Theoretically, we analyze the Jacobian spectral radius of the recursive operator to prove its global asymptotic stability. Numerical results demonstrate that RC-Flow reduces inference latency by two orders of magnitude while achieving a 2.7 dB performance gain in low signal-to-noise ratio regimes compared to the score-based baseline.

Problem

• MIMO channel estimation
• channel state information
• noisy measurements
• under-determined measurements
• spectral efficiency

Innovation

• Recursive Flow
• flow matching
• closed-loop refinement
• channel estimation
• massive MIMO