  This paper introduces a novel multi-user mobile edge computing (MEC) scheme
facilitated by the simultaneously transmitting and reflecting reconfigurable
intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Unlike
existing MEC approaches, the proposed scheme enables bidirectional offloading,
allowing users to concurrently offload tasks to the MEC servers located at the
ground base station (BS) and UAV with STAR-RIS support. Specifically, we
formulate an optimization problem aiming at maximizing the energy efficiency of
the system while ensuring the quality of service (QoS) constraints by jointly
optimizing the resource allocation, user scheduling, passive beamforming of the
STAR-RIS, and the UAV trajectory. A block coordinate descent (BCD) iterative
algorithm designed with the Dinkelbach's algorithm and the successive convex
approximation (SCA) technique is proposed to effectively handle the formulated
non-convex optimization problem with significant coupling among variables.
Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC
scheme possesses significant advantages in enhancing the system energy
efficiency over other baseline schemes including the conventional RIS-aided
scheme.
