  Correlation exploitation is essential for efficient multi-user channel
estimation (MUCE) in massive MIMO systems. However, the existing works either
rely on presumed strong correlation or learn the correlation through large
amount of labeled data, which are difficult to acquire in a real system. In
this paper, we propose an adaptive MUCE algorithm based on contrastive feature
learning. The contrastive learning (CL) is used to automatically learn the
similarity within channels by extracting the channel state information (CSI)
features based on location information. The similar features will be fed into
the downstream network to explore the strong correlations among CSI features to
improve the MUCE performance with a small number of labeled data. Simulation
results show that the contrastive feature learning can enhance the overall MUCE
performance with high training efficiency.
