Generalization-Enhanced Channel Estimation Through Adaptive Interpolation and Multi-Task Learning-Based Denoising Network

Accurate CSI estimation with low pilots is desirable for the multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. In the existing channel estimation methods, both interpolation and denoising suffer from the problem of generalization. In this paper, we propose an adaptive interpolation and multi-task learning denoising network for generalization-enhanced CSI estimation. First, we model the wireless channel based on Gaussian process (GP) and use Bayesian optimization (BO) to find the optimal parameters of the Mat´ern kernel for interpolation. For each matrix, we can find the most suitable parameters of the kernel to achieve precise interpolation adaptively. Then, we design the multi-task residual network (MT-Net) based on multi-task learning. In MT-Net, shared layers are employed to utilize the relevant information between multiple tasks. And task-specific layers are also designed to extract the characteristics of each task. Compared to single-task learning, MT-Net can achieve information sharing between multiple tasks to enhance the scenario generalization of the model. Simulation results show that when the application scenario changes, our method exhibits a stronger generalization ability compared to other neural network-assisted methods.