Joint Hybrid 3D Beamforming Relying on Sensor-Based Training for Reconfigurable Intelligent Surface Aided TeraHertz-Based Multiuser Massive MIMO Systems

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a joint hybrid 3D beamformer relying on sophisticated sensor-based beam training and channel estimation is proposed for Reconfigurable Intelligent Surface (RIS) aided THz Multi-user Massive Multiple Input Multiple Output (MIMO) systems. A novel joint subarray based THz base station (BS) architecture and the corresponding sub-RIS is proposed. The BS, RIS and receiver antenna arrays of the users are all Uniform Planar Arrays (UPAs). Moreover, the conditions of maintaining the orthogonality of the proposed joint architecture are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field path-loss are also derived. The Ultra-wideband (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation. The optimal active and passive beamforming schemes are also derived. Moreover, Precise Beamforming Algorithm (PBA) for joint RIS phase shift and user equipment (UE) receiver beamforming is proposed, which further improves the beamforming accuracy by circumventing the performance limitations imposed by positioning errors. Our simulation results show that the proposed system significantly improves the spectral efficiency, despite its low complexity. Compared to the scheme operating without PBA, our proposed scheme increases the spectral efficiency on average by 10.41%, 10.17%, and 5.19% for the three farfield configurations, and by 5.05% and 3.95% for the two nearfield configurations, respectively. This makes our solution eminently suitable for delay-sensitive applications.