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This paper investigates the potential of contrastive learning in 6Gultra-massive multiple-input multiple-output (UM-MIMO) communication systems,specifically focusing on hybrid beamforming under imperfect channel stateinformation (CSI) conditions at THz. UM-MIMO systems are promising for future6G wireless communication networks due to their high spectral efficiency andcapacity. The accuracy of CSI significantly influences the performance ofUM-MIMO systems. However, acquiring perfect CSI is challenging due to variouspractical constraints such as channel estimation errors, feedback delays, andhardware imperfections. To address this issue, we propose a novelself-supervised contrastive learning-based approach for hybrid beamforming,which is robust against imperfect CSI. We demonstrate the power of contrastivelearning to tackle the challenges posed by imperfect CSI and show that ourproposed method results in improved system performance in terms of achievablerate compared to traditional methods.

Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI