Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Contrastive CSI Representation Learning for Massive MIMO
  Positioning   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.

36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio