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Future wireless services, such as the metaverse require high informationrate, reliability, and low latency. Multi-user wireless systems can meet suchrequirements by utilizing the abundant terahertz bandwidth with a massivenumber of antennas, creating narrow beamforming solutions. However, existingsolutions lack proper modeling of channel dynamics, resulting in inaccuratebeamforming solutions in high-mobility scenarios. Herein, a dynamic,semantically aware beamforming solution is proposed for the first time,utilizing novel artificial intelligence algorithms in variational causalinference to compute the time-varying dynamics of the causal representation ofmulti-modal data and the beamforming. Simulations show that the proposedcausality-guided approach for Terahertz (THz) beamforming outperforms classicalMIMO beamforming techniques.

Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?