  In this paper, a channel estimation technique for reconfigurable intelligent
surface (RIS)-aided multi-user multiple-input single-output communication
systems is proposed. By deploying a small number of active elements at the RIS,
the RIS can receive and process the training signals. Through the partial
channel state information (CSI) obtained from the active elements, the overall
training overhead to estimate the entire channel can be dramatically reduced.
To minimize the estimation complexity, the proposed technique is based on the
linear combination of partial CSI, which only requires linear matrix
operations. By exploiting the spatial correlation among the RIS elements,
proper weights for the linear combination and normalization factors are
developed. Numerical results show that the proposed technique outperforms other
schemes using the active elements at the RIS in terms of the normalized mean
squared error when the number of active elements is small, which is necessary
to maintain the low cost and power consumption of RIS.
