Multi-Task Learning-Based Channel Estimation for RIS Assisted Multi-User Communication Systems

In this letter, we propose a multi-task learning (MTL)-based joint channel estimation scheme for reconfigurable intelligent surface (RIS) assisted millimeter-wave communication system, where the direct channel (DC) and cascaded channel (CC) are estimated at the same coherence time by learning the feature of shared pilots. Since the dimension of CC is much larger than the DC, we design a learnable joint loss function based on homoscedastic task uncertainty to balance the training of two subtasks. Meanwhile, the residual shrinkage blocks are introduced into the multi-task network architecture to release the noise effect. Simulation results show that the estimation accuracy of MTL with less pilot overhead outperforms conventional channel estimation scheme, and significantly reduces training overhead compared with the single-task network.