  The aerial-terrestrial communication system constitutes an efficient paradigm
for supporting and complementing terrestrial communications. However, the
benefits of such a system cannot be fully exploited, especially when the
line-of-sight (LoS) transmissions are prone to severe deterioration due to
complex propagation environments in urban areas. The emerging technology of
reconfigurable intelligent surfaces (RISs) has recently become a potential
solution to mitigate propagation-induced impairments and improve wireless
network coverage. Motivated by these considerations, in this paper, we address
the coverage and link performance problems of the aerial-terrestrial
communication system by proposing an RIS-assisted transmission strategy. In
particular, we design an adaptive RIS-assisted transmission protocol, in which
the channel estimation, transmission strategy, and data transmission are
independently implemented in a frame. On this basis, we formulate an
RIS-assisted transmission strategy optimization problem as a mixed-integer
non-linear program (MINLP) to maximize the overall system throughput. We then
employ multi-task learning to speed up the solution to the problem. Benefiting
from multi-task learning, the computation time is reduced by about four orders
of magnitude. Numerical results show that the proposed RIS-assisted
transmission protocol significantly improves the system throughput and reduces
the transmit power.
