论文标题
通过元学习和深度denoising进行大规模的MIMO通道预测：小数据集足够了吗？
Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?
论文作者
论文摘要
准确的通道知识对于大量多输入多输出（MIMO）至关重要，这激发了通道预测的使用。通道预测的机器学习技术具有很大的希望，但是目前的方案的适应环境变化的能力受到限制，因为它们需要大型培训开销。为了准确预测新环境的无线通道，并通过减少训练开销，我们提出了一种基于用于大规模MIMO通信的元学习算法的快速自适应通道预测技术。我们利用模型 - 不合稳定的元学习（MAML）算法来使用少量的标记数据来快速适应。此外，为了提高预测准确性，我们通过使用深层图像（DIP）采用训练数据的降解过程。数值结果表明，仅使用少数几个微调样本，提出的基于MAML的通道预测指标可以提高预测准确性。基于倾斜的降解过程可在信道预测中获得额外的增长，尤其是在低信噪比的比率方面。
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.
