Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?
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.