Self-Supervised Learning Enhanced Channel Estimation in Massive MIMO System With Low-Resolution ADCs

Traditional deep learning-based end-to-end (E2E) channel estimation approaches typically require extensive paired data for offline training, which is even more demanding when the signal is quantized by low-resolution analog-digital converters (ADCs), making E2E channel estimation impractical. Motivated by the above, we propose a model-driven self-supervised framework aimed at estimating channel information in massive multiple-input multiple-output systems. By applying the additive quantization noise model (AQNM) and the Bussgang decomposition theorem, we reformulate the nonlinear quantization process of low-resolution ADCs into a representation that resembles additive noise. Afterward, we integrate the self-supervised framework into the minimum mean squared error (MMSE) and Bussgang linear MMSE (BLMMSE) methods to refine channel estimation in low-resolution ADC scenarios. Notably, our approach has the advantage of not requiring true data labels, making it more adaptable to new scenarios compared to traditional E2E methods. Simulation results reveal that our approach using limited data outperforms existing supervised-learning approaches and exhibits an inherent robustness and generalization capabilities.