  Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial
vehicle (UAV) holds promise for improving traditional terrestrial network
performance. Unlike conventional methods deploying passive RIS on UAVs, this
study delves into the efficacy of an aerial active RIS (AARIS). Specifically,
the downlink transmission of an AARIS network is investigated, where the base
station (BS) leverages rate-splitting multiple access (RSMA) for effective
interference management and benefits from the support of an AARIS for jointly
amplifying and reflecting the BS's transmit signals. Considering both the
non-trivial energy consumption of the active RIS and the limited energy storage
of the UAV, we propose an innovative element selection strategy for optimizing
the on/off status of RIS elements, which adaptively and remarkably manages the
system's power consumption. To this end, a resource management problem is
formulated, aiming to maximize the system energy efficiency (EE) by jointly
optimizing the transmit beamforming at the BS, the element activation, the
phase shift and the amplification factor at the RIS, the RSMA common data rate
at users, as well as the UAV's trajectory. Due to the dynamicity nature of UAV
and user mobility, a deep reinforcement learning (DRL) algorithm is designed
for resource allocation, utilizing meta-learning to adaptively handle fast
time-varying system dynamics. Simulations indicate that incorporating an active
RIS at the UAV leads to substantial EE gain, compared to passive RIS-aided UAV.
We observe the superiority of the RSMA-based AARIS system in terms of EE,
compared to existing approaches adopting non-orthogonal multiple access (NOMA).
