  We study the collaborative image retrieval problem at the wireless edge,
where multiple edge devices capture images of the same object, which are then
used jointly to retrieve similar images at the edge server over a shared
multiple access channel. We propose a semantic non-orthogonal multiple access
(NOMA) communication paradigm, in which extracted features from each device are
mapped directly to channel inputs, which are then added over-the-air. We
propose a novel contrastive learning (CL)-based semantic communication (CL-SC)
paradigm, aiming to exploit signal correlations to maximize the retrieval
accuracy under a total bandwidth constraints. Specifically, we treat noisy
correlated signals as different augmentations of a common identity, and propose
a cross-view CL algorithm to optimize the correlated signals in a
coarse-to-fine fashion to improve retrieval accuracy. Extensive numerical
experiments verify that our method achieves the state-of-the-art performance
and can significantly improve retrieval accuracy, with particularly significant
gains in low signla-to-noise ratio (SNR) and limited bandwidth regimes.
