Unsupervised Deep Equilibrium Model Learning for Large-Scale Channel Estimation with Performance Guarantees
Haotian Tian, Lixiang Lian

TL;DR
This paper introduces an unsupervised deep equilibrium model for large-scale channel estimation that leverages GSURE for loss calculation, providing performance guarantees without needing ground-truth channels.
Contribution
It proposes a novel unsupervised learning framework using DEQ models and GSURE, with theoretical performance guarantees and improved results over baselines.
Findings
Outperforms baselines without ground-truth channels
Provides theoretical guarantees for MSE performance
Ensures compressible solutions via DEQ architecture
Abstract
Supervised deep learning methods have shown promise for large-scale channel estimation (LCE), but their reliance on ground-truth channel labels greatly limits their practicality in real-world systems. In this paper, we propose an unsupervised learning framework for LCE that does not require ground-truth channels. The proposed approach leverages Generalized Stein's Unbiased Risk Estimate (GSURE) as a principled unsupervised loss function, which provides an unbiased estimate of the projected mean-squared error (PMSE) from compressed noisy measurements. To ensure a guaranteed performance, we integrate a deep equilibrium (DEQ) model, which implicitly represents an infinite-depth network by directly learning the fixed point of a parameterized iterative process. We theoretically prove that, under mild conditions, the proposed GSURE-based unsupervised DEQ learning can achieve oracle-level…
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