  In this work, we address the issue of quality of experience (QoE) in unmanned
aerial vehicle (UAV) aided multiuser rate-splitting multiple access (RSMA)
networks under secrecy constraints. The problem is formulated as maximization
of sum mean opinion scores (MOSs) of the users. The problem is decomposed into
two subproblems, beamforming and rate allocation and UAV trajectory subproblem.
For, beamforming and rate allocation subproblem, we use the epigraph method,
property of polynomials, and the norm-bounded error of channels, we linearize
the objective function. Then, applying second-order conic (SOC) and first
Taylor expansion, we convexify the remaining nonconvex constraints. For the
highly nonconvex UAV trajectory, we unroll the constraints and we apply first
Taylor expansion on the unrolled constraints. The simulation results
demonstrate the efficiency of the proposed framework.
