The MPComm framework decomposes a bi-objective optimization into outer timing selection via graph search and inner power/channel allocation with asymptotic optimality, yielding up to 6x less terrestrial interference and 6dB energy savings under timeliness constraints.
Deep reinforcement learning for AoI-aware trajectory and phase-shift design in IRS-assisted UA V data collection,
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Model Predictive Communication for Timely Status Updates in Low-Altitude Networks
The MPComm framework decomposes a bi-objective optimization into outer timing selection via graph search and inner power/channel allocation with asymptotic optimality, yielding up to 6x less terrestrial interference and 6dB energy savings under timeliness constraints.