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Fig. 4 | EURASIP Journal on Wireless Communications and Networking

Fig. 4

From: Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training

Fig. 4

Overview of the proposed SE-QPTNet. Proposed SE-QPTNet compensates for the effects of propagation delay by separately dealing with the sub-antenna arrays. Practically, the intermediate beam codes \(\varvec{\hat{z}}\) are divided into \(\varvec{\hat{z}}_{\le t}\) as small scale parts and \(\varvec{\hat{z}}_{>t}\) as large-scale parts by an anchor t. We feed \(\varvec{\hat{z}}_{\le t}\) to the spatial attention-associated beam prediction for identifying the beam signature vector \(\varvec{c}_{t}\). Contrastive environmental prediction measures the similarity between the \(\varvec{c}_{t}\) and the reconstructed \(\varvec{\hat{H}}_\mathrm{{{recon}}}\), and makes the negative samples \(\varvec{\hat{H}}_\mathrm{{{neg}}}\) dissimilar to its beam signature

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