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

arXiv: https://arxiv.org/abs/2406.19871 · HTML: https://mlsanity.com/paper/2406.19871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems](https://mlsanity.com/paper/2003.12671/) (2020)
- [Optimal Virtual Network Function Placement and Resource Allocation in Multi-Cloud Service Function Chaining Architecture](https://mlsanity.com/paper/1903.11550/) (2019)
- [Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems](https://mlsanity.com/paper/1906.09548/) (2019)

## Abstract (arXiv)

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.