# RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility

arXiv: https://arxiv.org/abs/2403.05133 · HTML: https://mlsanity.com/paper/2403.05133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Clustered Vehicular Federated Learning: Process and Optimization](https://mlsanity.com/paper/2201.11271/) (2022)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [Intelligent Task Offloading for Heterogeneous V2X Communications](https://mlsanity.com/paper/2006.15855/) (2020)
- [Robust Secure UAV Communications with the Aid of Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2008.09404/) (2020)
- [Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles](https://mlsanity.com/paper/2310.00384/) (2023)
- [Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach](https://mlsanity.com/paper/2311.16116/) (2023)
- [Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing](https://mlsanity.com/paper/2208.04509/) (2022)
- [RIS-Aided Wireless Communications: Extra Degrees of Freedom via Rotation and Location Optimization](https://mlsanity.com/paper/2202.05569/) (2022)
- [Multiple RISs Assisted Cell-Free Networks With Two-timescale CSI: Performance Analysis and System Design](https://mlsanity.com/paper/2208.06072/) (2022)

## Abstract (arXiv)

Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for autonomous aerial driving. However, UAM encounters the intelligent perception challenge: high perception learning requirements conflict with the limited sensors and computing chips of flying cars. To overcome the challenge, federated learning (FL) and other collaborative learning have been proposed to enable resource-limited devices to conduct onboard deep learning (DL) collaboratively. But traditional collaborative learning like FL relies on a central integrator for DL model aggregation, which is difficult to deploy in dynamic environments. The fully decentralized learning schemes may be the intuitive solution while the convergence of distributed learning cannot be guaranteed. Accordingly, this paper explores reconfigurable intelligent surfaces (RIS) empowered distributed learning, taking account of topological attributes to facilitate the learning performance with convergence guarantee. We propose several FL topological criteria for optimizing the transmission delay and convergence rate by exploiting the Laplacian matrix eigenvalues of the communication network. Subsequently, we innovatively leverage the RIS link modification ability to remold the current network according to the proposed topological criteria. This paper rethinks the functions of RIS from the perspective of the network layer. Furthermore, a deep deterministic policy gradient-based RIS phase shift control algorithm is developed to construct or deconstruct the network links simultaneously to reshape the communication network. Simulation experiments are conducted over MobileNet-based multi-view learning to verify the efficiency of the distributed FL framework.