Meta-Learning-Based Channel Denoising for MIMO-OFDM Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems often suffer from channel estimation errors due to the limited number of reference signals and channel selectivity in both the time and frequency domains. To address these errors, this paper proposes a meta-learning-based channel denoising method for MIMO-OFDM systems. The proposed method employs a denoising convolutional neural network (DnCNN) to mitigate estimation errors in channel frequency responses (CFRs) across the time-frequency domain. For training the DnCNN, a model-agnostic meta-learning (MAML) approach is devised which enables fast adaptation of the DnCNN to new and unseen channel denoising tasks. In our MAML approach, training samples from past user equipments (UEs) are used to pre-train the DnCNN, following the MAML principle. After pre-training, a few training samples from new UEs are utilized to fine-tune the pre-trained DnCNN using only a few gradient steps. A practical data generation strategy is also presented, substituting true CFRs with high-quality CFR estimates determined via data-aided channel estimation. Simulation results show that the proposed channel denoising method effectively reduces the channel estimation errors of conventional techniques and adapts well to new and unseen channel environments.