Fig. 3From: Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam trainingOverview of the proposed QPTNet. CSI is separately processed along the frequency subcarriers to capture the continuous spatial feature \(\varvec{z}_{l}\) via the GRU encoder. A latent beam codebook handles the perception of environmental variations by exploring the relation between the categorical beam \(\varvec{g}^{*}_{i}\) and the continuous spatial feature. Moreover, we reconstruct the channel data based on the selected latent beams via a GRU decoder to update the codebook and hold the consistency of channel and beamspace. The selected latent beams are fed into a spatial attention-associated beam prediction. \(\varvec{c}_t\) indicates the global beam signatureBack to article page