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

Alapfogalmak

The author proposes a reconfigurable intelligent surface (RIS) empowered distributed learning approach to optimize convergence and transmission delay in urban air mobility. By leveraging RIS, the communication network is reshaped to enhance distributed federated learning performance.

Kivonat

The paper introduces a novel approach using RIS to improve distributed learning in urban air mobility. It explores topological criteria and phase shift control algorithms to optimize convergence and transmission rates. Simulation experiments validate the efficiency of the proposed framework.

The study addresses challenges in distributed learning for urban air mobility by utilizing RIS technology. It focuses on optimizing communication networks through topology control and phase shift adjustments. The proposed method enhances convergence and reduces transmission delays for efficient distributed learning.

Key points include:

• Introduction of RIS-empowered distributed learning for urban air mobility.
• Exploration of topological criteria and phase shift control algorithms.
• Validation through simulation experiments on multi-view learning datasets.
• Focus on improving convergence and reducing transmission delays in distributed federated learning.

Statisztikák

Comprehensive scene perception requires multi-view data and computing resources.
FL relies on central integrator for DL model aggregation.
RIS can reflect signals toward users experiencing direct link blockages.
Proposed DDPG-based RIS phase shift control algorithm optimizes network links.
Simulation experiments conducted over MobileNet-based multi-view learning.

Idézetek

"The proposed scheme construct and deconstruct the communication link by accommodating the transmission rate."
"RIS with massive reflective elements can tune the phase shifts of impinging electromagnetic waves."
"Simulation experiments are conducted over MobileNet-based multi-view learning to verify the efficiency of the distributed FL framework."

Főbb Kivonatok

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

by Kai Xiong,Ru... : arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05133.pdf

Mélyebb kérdések

How does the use of RIS technology impact scalability in urban air mobility systems

The use of Reconfigurable Intelligent Surfaces (RIS) technology has a significant impact on scalability in urban air mobility systems. By leveraging RIS to optimize communication topologies and enhance transmission rates, the system can accommodate a larger number of vehicles without compromising performance. The ability of RIS to dynamically adjust signal reflections and improve connectivity between flying cars enables seamless integration of new vehicles into the network. This scalability is crucial for expanding urban air mobility operations efficiently and effectively.

What potential challenges could arise from fully decentralized deep learning models without central integration

Fully decentralized deep learning models without central integration pose several potential challenges. One major challenge is ensuring consistent parameter consensus among distributed participants in the absence of a central integrator. Inconsistent parameters can lead to corrupted learning performance and hinder convergence in distributed learning scenarios. Additionally, managing communication overheads and delays becomes more complex without a centralized entity coordinating model aggregation. Ensuring synchronization, data consistency, and efficient resource utilization are key challenges that arise from fully decentralized deep learning models.

How might advancements in RIS technology influence other industries beyond urban air mobility

Advancements in Reconfigurable Intelligent Surfaces (RIS) technology have the potential to influence various industries beyond urban air mobility. In telecommunications, RIS can revolutionize wireless communication by enhancing signal strength, reducing interference, and improving coverage areas. This could lead to faster data transfer speeds, better network reliability, and enhanced connectivity for users across different environments.
Moreover, in smart infrastructure development such as smart cities or IoT applications, RIS can play a vital role in optimizing wireless networks' performance by adjusting signal propagation intelligently based on environmental conditions or user requirements. This could result in more efficient energy consumption, improved network security measures through controlled signal reflection patterns.
Overall, advancements in RIS technology have far-reaching implications for diverse industries where reliable wireless communication plays a critical role in operational efficiency and service delivery.