Performance Optimization for Environment-Adaptive Minimum Number of STAR-RISs Aided TeraHertz Multi-User Ultra-Massive MIMO Systems

Leveraging terahertz (THz) frequency bands for communication presents a compelling solution to achieve exceedingly high data rates in the forthcoming era of wireless networks. Although traditional THz communication suffers from atmospheric absorption and sensitivity to blockages, employing cooperative directional transmissions assisted byReconfigurable Intelligent Surfaces(RISs) can effectively extend the communication range. However, the propagation environments including obstacle distributions have not been taken into account so far, and the minimum number of RIS deployment with separated channel estimation has not been solved yet. To meet the challenges, this paper proposes a novel system performance optimization scheme for environment-adaptive minimum number ofSimultaneously Transmitting and Reflecting(STAR) RISs assisted multi-user THz ultra-massive MIMO systems. We also propose a multi-STAR separated channel estimation algorithm based on recursive tensor decomposition and semi-passive architecture for each STAR, which efficiently eliminates the ambiguity in cascaded channel estimation, facilitating the optimal multi-STAR beamforming. Simulation results show that the proposed scheme improves the system performance dramatically compared to the benchmarks. Furthermore, the proposed scheme improves the system performance on average by 54.6% and 34.9% for two configurations of two STARs and 20% for three-STAR configuration compared to the scenario with a 10% deviation from the optimal position of the STAR and perfectchannel state information(CSI).