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Terahertz (THz) systems have the benefit of high bandwidth and hence arecapable of supporting ultra-high data rates, albeit at the cost of highpathloss. Hence they tend to harness high-gain beamforming. Therefore a novelhybrid 3D beamformer relying on sophisticated sensor-based beam training andchannel estimation is proposed for Reconfigurable Intelligent Surface (RIS)aided THz MIMO systems. A so-called array-of-subarray based THz BS architectureis adopted and the corresponding sub-RIS structure is proposed. The BS, RIS andreceiver antenna arrays of the users are all uniform planar arrays (UPAs). TheUltra-wideband (UWB) sensors are integrated into the RIS and the user locationinformation obtained by the UWB sensors is exploited for channel estimation andbeamforming. Furthermore, the novel concept of a Precise Beamforming Algorithm(PBA) is proposed, which further improves the beam-forming accuracy bycircumventing the performance limitations imposed by positioning errors.Moreover, the conditions of maintaining the orthogonality of the RIS-aided THzchannel are derived in support of spatial multiplexing. The closed-formexpressions of the near-field and far-field path-loss are also derived. Oursimulation results show that the proposed scheme accurately estimates theRIS-aided THz channel and the spectral efficiency is much improved, despite itslow complexity. This makes our solution eminently suitable for delay-sensitiveapplications.

Hybrid 3D Beamforming Relying on Sensor-Based Training and Channel Estimation for Reconfigurable Intelligent Surface Aided TeraHertz MIMO systems