  Terahertz (THz) systems are capable of supporting ultra-high data rates
thanks to large bandwidth, and the potential to harness high-gain beamforming
to combat high pathloss. In this paper, a novel quantum sensing (Ghost Imaging
(GI)) based beam training is proposed for Simultaneously Transmitting and
Reflecting Reconfigurable Intelligent Surface (STAR RIS) aided THz multi-user
massive MIMO systems. We first conduct GI by surrounding 5G downlink signals to
obtain 3D images of the environment including users and obstacles. Based on the
information, we calculate the optimal position of the UAV-mounted STAR by the
proposed algorithm. Thus the position-based beam training can be performed. To
enhance the beam-forming gain, we further combine with channel estimation and
propose a semi-passive structure of the STAR and ambiguity elimination scheme
for separated channel estimation. Thus the ambiguity in cascaded channel
estimation, which may affect optimal passive beamforming, is avoided. The
optimal active and passive beamforming are then carried out and data
transmission is initiated. The proposed BS sub-array and sub-STAR spatial
multiplexing architecture, optimal active and passive beamforming, digital
precoding, and optimal position of the UAV- mounted STAR are investigated
jointly to maximize the average achievable sum rate of the users. Moreover, the
cloud radio access networks (CRAN) structured 5G downlink signal is proposed
for GI with enhanced resolution. The simulation results show that the proposed
scheme achieves beam training and separated channel estimation efficiently, and
increases the spectral efficiency dramatically compared to the case when the
STAR operates with random phase.
