Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model
Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress. This paper views the evolution of AAV kinematics, channel state, and user backlog as a structured differentiable world model and develops Learn for Variation (L4V) to exploit that model efficiently. L4V replaces a discontinuous completion-time objective with a cumulative-backlog surrogate, unrolls the mission dynamics, and propagates pathwise sensitivities to a neural policy through the discrete adjoint recursion. The resulting derivative is exact conditional on a fixed exogenous-noise realizat...
| Authors | Xiucheng Wang, Zhenye Chen, Nan Cheng et al. |
|---|---|
| Primary category | eess.SY |
| Published | 19 March 2026 |
| Last updated at source | 20 August 2026 |
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