Fast Online Channel Estimation in Massive MIMO: A Zero-Shot Self-Supervised Approach

Abstract

With the growing number of antennas in massive multiple-input multiple-output (MIMO) systems, robust and fast channel estimation becomes increasingly critical yet remains highly challenging. In this work, we propose a lightweight zero-shot self-supervised (ZS-SS) learning framework. It leverages non-local self-similarity in wireless channels to construct a channel-coefficient bank and generate training pairs via randomized and non-contiguous spatial permutations to decorrelate noise. These pairs then train a compact convolutional neural network (CNN) with a specially designed composite loss for robust channel estimation. To further improve adaptability and efficiency, we incorporate a meta-learning approach for fast inference time to dynamic channel environments. Simulations under Gaussian and representative non-Gaussian scenarios show that our method achieves up to 90% gains over traditional estimators and consistent improvements over state-of-the-art baselines, while running nearly 100 times faster. This demonstrates its practicality and suitability for real-time deployment in resource-limited massive MIMO systems.