Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?
This work addresses the need for efficient channel prediction in massive MIMO systems, enabling quick adaptation to environmental changes with reduced training overhead.
The paper proposes a meta-learning-based channel prediction method for massive MIMO that adapts to new environments with few training samples, achieving improved prediction accuracy. The method uses MAML for fast adaptation and DIP for denoising, showing gains especially in low SNR regimes.
Accurate channel knowledge is critical in massive multiple-input multiple-output (MIMO), which motivates the use of channel prediction. Machine learning techniques for channel prediction hold much promise, but current schemes are limited in their ability to adapt to changes in the environment because they require large training overheads. To accurately predict wireless channels for new environments with reduced training overhead, we propose a fast adaptive channel prediction technique based on a meta-learning algorithm for massive MIMO communications. We exploit the model-agnostic meta-learning (MAML) algorithm to achieve quick adaptation with a small amount of labeled data. Also, to improve the prediction accuracy, we adopt the denoising process for the training data by using deep image prior (DIP). Numerical results show that the proposed MAML-based channel predictor can improve the prediction accuracy with only a few fine-tuning samples. The DIP-based denoising process gives an additional gain in channel prediction, especially in low signal-to-noise ratio regimes.