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Accurate channel knowledge is critical in massive multiple-inputmultiple-output (MIMO), which motivates the use of channel prediction. Machinelearning techniques for channel prediction hold much promise, but currentschemes are limited in their ability to adapt to changes in the environmentbecause they require large training overheads. To accurately predict wirelesschannels for new environments with reduced training overhead, we propose a fastadaptive channel prediction technique based on a meta-learning algorithm formassive 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 forthe training data by using deep image prior (DIP). Numerical results show thatthe proposed MAML-based channel predictor can improve the prediction accuracywith only a few fine-tuning samples. The DIP-based denoising process gives anadditional gain in channel prediction, especially in low signal-to-noise ratioregimes.

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