Multi-hop RIS-aided Learning Model Sharing for Urban Air Mobility

TL;DR

This paper proposes a multi-hop RIS-assisted model sharing framework for urban air mobility, improving deep learning model transfer between flying cars and enhancing onboard learning efficiency in diverse environments.

Contribution

It introduces a multi-hop RIS technology combined with knowledge distillation to facilitate efficient DL model sharing among flying vehicles in UAM.

Findings

Achieves 85% higher total reward compared to benchmarks.

Enhances model sharing efficiency in diverse urban air environments.

Reduces data collection and training costs for flying cars.

Abstract

Urban Air Mobility (UAM), powered by flying cars, is poised to revolutionize urban transportation by expanding vehicle travel from the ground to the air. This advancement promises to alleviate congestion and enable faster commutes. However, the fast travel speeds mean vehicles will encounter vastly different environments during a single journey. As a result, onboard learning systems need access to extensive environmental data, leading to high costs in data collection and training. These demands conflict with the limited in-vehicle computing and battery resources. Fortunately, learning model sharing offers a solution. Well-trained local Deep Learning (DL) models can be shared with other vehicles, reducing the need for redundant data collection and training. However, this sharing process relies heavily on efficient vehicular communications in UAM. To address these challenges, this paper leverages the multi-hop Reconfigurable Intelligent Surface (RIS) technology to improve DL model sharing between distant flying cars. We also employ knowledge distillation to reduce the size of the shared DL models and enable efficient integration of non-identical models at the receiver. Our approach enhances model sharing and onboard learning performance for cars entering new environments. Simulation results show that our scheme improves the total reward by 85% compared to benchmark methods.