  Similarity metric is crucial for massive MIMO positioning utilizing channel
state information~(CSI). In this letter, we propose a novel massive MIMO CSI
similarity learning method via deep convolutional neural network~(DCNN) and
contrastive learning. A contrastive loss function is designed considering
multiple positive and negative CSI samples drawn from a training dataset. The
DCNN encoder is trained using the loss so that positive samples are mapped to
points close to the anchor's encoding, while encodings of negative samples are
kept away from the anchor's in the representation space. Evaluation results of
fingerprint-based positioning on a real-world CSI dataset show that the learned
similarity metric improves positioning accuracy significantly compared with
other known state-of-the-art methods.
