Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system

TL;DR

This paper introduces a Koopman-based trajectory model combined with a greedy resource allocation strategy to optimize computation offloading in high-mobility ISAC-enabled IoT systems, aiming to reduce energy consumption and improve prediction accuracy.

Contribution

It proposes a novel Koopman-based trajectory prediction model integrated with a greedy optimization approach for resource allocation in high-mobility IoT networks.

Findings

Potential 33% energy savings after 1000 iterations

Improved trajectory prediction accuracy with Koopman model

Identifies key challenges in velocity prediction and model division

Abstract

User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33\% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase.