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

Abstract

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-tonoise ratio regimes.

Enhanced AI-Based CSI Prediction Solutions for Massive MIMO in 5G and 6G Systems

“…In [25], the authors propose a temporal frame structure for updating the network. In [32], a meta-learning solution is proposed that permits a fast fine-tuning process in new environments. Although updating the networks "on-the-fly" is interesting and might have a significant future impact, the anticipated signaling overhead and processing load make the real-time model update very challenging in the current and near-future systems.…”