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Abstract

Uncrewed aerial vehicle (UAV) communication systems are increasingly used to provide temporary connectivity when ground communication infrastructure is damaged by disasters such as hurricanes and wildfires. However, the amount of aerial support required changes as ground networks recover and communication demand evolves. We focus on dynamically adjusting the number and allocation of actively deployed UAVs within a fixed available fleet. We develop a model predictive control (MPC) framework for dynamic fleet sizing of heterogeneous UAV relays during post-disaster communication recovery. The framework accounts for communication demand, ground network recovery, heterogeneous UAV operation, UAV energy availability, and backhaul capacity, which limits how much relayed traffic can be transmitted to the external communication network. The resulting formulation is a convex quadratic program that supports efficient online fleet sizing and deployment decisions. We evaluate the framework using seven historical disaster recovery case studies based on Federal Communications Commission reports, together with four backhaul scenarios and sensitivity analyses. Results show that required active UAV fleet size varies considerably throughout recovery, while backhaul capacity and fleet composition affect communication performance, highlighting the value of adapting active fleet size over time.