# Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs

arXiv: https://arxiv.org/abs/2209.07655 · HTML: https://mlsanity.com/paper/2209.07655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing](https://mlsanity.com/paper/2009.11277/) (2020)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [Fundamental Tradeoffs in Communication and Trajectory Design for UAV-Enabled Wireless Network](https://mlsanity.com/paper/1805.07038/) (2018)
- [Average Peak Age-of-Information Minimization in UAV-assisted IoT Networks](https://mlsanity.com/paper/1804.06543/) (2018)
- [Power-Constrained Trajectory Optimization for Wireless UAV Relays with Random Requests](https://mlsanity.com/paper/2002.09617/) (2020)
- [MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms](https://mlsanity.com/paper/2007.01228/) (2020)

## Abstract (arXiv)

We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station. Our goal is to minimize the time-average service latencies involved in handling transmission requests from ground users under Poisson arrivals, subject to an average UAV power constraint. Equipped with rate adaptation to efficiently leverage air-to-ground channel stochastics, we first derive the optimal control policy for a single relay via a semi-Markov decision process formulation, with competitive swarm optimization for UAV trajectory design. Accordingly, we detail a multiscale decomposition of this construction: outer decisions on radial wait velocities and end positions optimize the expected long-term delay-power trade-off; consequently, inner decisions on angular wait velocities, service schedules, and UAV trajectories greedily minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms via replication and consensus-driven command-and-control, this policy is embedded with spread maximization and conflict resolution heuristics. We demonstrate that our framework offers superior performance with respect to average service latencies and average per-UAV power consumption: 11x faster data payload delivery relative to static UAV-relay deployments and 2x faster than a deep-Q network solution; remarkably, one relay with our scheme outclasses three relays under a joint successive convex approximation policy by 62%.