Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?
This addresses reliability issues for future wireless services like the metaverse, though it appears incremental as it builds on existing beamforming with new modeling.
The paper tackled the problem of inaccurate beamforming in high-mobility terahertz wireless systems by proposing a dynamic, semantically aware beamforming solution using variational causal inference, which outperformed classical MIMO techniques in simulations.
Future wireless services, such as the metaverse require high information rate, reliability, and low latency. Multi-user wireless systems can meet such requirements by utilizing the abundant terahertz bandwidth with a massive number of antennas, creating narrow beamforming solutions. However, existing solutions lack proper modeling of channel dynamics, resulting in inaccurate beamforming 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 causal inference to compute the time-varying dynamics of the causal representation of multi-modal data and the beamforming. Simulations show that the proposed causality-guided approach for Terahertz (THz) beamforming outperforms classical MIMO beamforming techniques.