A new method for estimating wireless channel information in multiple-input multiple-output (MIMO) systems is proposed by researchers. The approach, called structure-informed hybrid estimation, uses tensor decomposition to improve accuracy and reduce the need for pilot signals. Two types of decomposition, canonical polyadic and Tucker, are compared, with Tucker showing better performance at low pilot densities. A neural network is also used to learn residual components and compensate for scattering and hardware imperfections. The results show significant improvements in estimation accuracy over existing methods. --- Why it matters: This matters because accurate channel state information is crucial for reliable communication in wireless systems, especially as bandwidths increase towards 6G. Improving estimation methods can lead to better network performance and more efficient use of resources. Source: https://arxiv.org/abs/2602.04083