# Energy-Efficient Cellular-Connected UAV Swarm Control Optimization

arXiv: https://arxiv.org/abs/2303.10398 · HTML: https://mlsanity.com/paper/2303.10398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 18 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Responsive Safety in Reinforcement Learning by PID Lagrangian Methods](https://mlsanity.com/paper/2007.03964/) (2020)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks](https://mlsanity.com/paper/1801.05000/) (2018)
- [Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs](https://mlsanity.com/paper/1509.01187/) (2015)
- [Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks](https://mlsanity.com/paper/1603.00300/) (2016)
- [The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1707.07534/) (2017)
- [UAV-to-UAV Communications in Cellular Networks](https://mlsanity.com/paper/1912.07534/) (2019)
- [Towards Reliable UAV Swarm Communication in D2D-Enhanced Cellular Network](https://mlsanity.com/paper/2002.04897/) (2020)
- [Correlation-aware Cooperative Multigroup Broadcast 360deg Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2010.11347/) (2020)
- [Real-time Video Streaming and Control of Cellular-Connected UAV System: Prototype and Performance Evaluation](https://mlsanity.com/paper/2101.10736/) (2021)

## Built on by (most cited first, 1 of 1)

- [Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking](https://mlsanity.com/paper/2401.12079/) (2024)

## Abstract (arXiv)

Cellular-connected unmanned aerial vehicle (UAV) swarm is a promising solution for diverse applications, including cargo delivery and traffic control. However, it is still challenging to communicate with and control the UAV swarm with high reliability, low latency, and high energy efficiency. In this paper, we propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network, where the ground base station (GBS) broadcasts the common C&C message in Phase I. In Phase II, the UAVs that have successfully decoded the C&C message will relay the message to the rest of UAVs via device-to-device (D2D) communications in either broadcast or unicast mode, under latency and energy constraints. To maximize the number of UAVs that receive the message successfully within the latency and energy constraints, we formulate the problem as a Constrained Markov Decision Process to find the optimal policy. To address this problem, we propose a decentralized constrained graph attention multi-agent Deep-Q-network (DCGA-MADQN) algorithm based on Lagrangian primal-dual policy optimization, where a PID-controller algorithm is utilized to update the Lagrange Multiplier. Simulation results show that our algorithm could maximize the number of UAVs that successfully receive the common C&C under energy constraints.