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.

翻译：移动设备的用户体验受限于有限的电池容量和处理能力，但6G技术的进步正迅速推动移动技术演进。移动边缘计算（MEC）提供了一种解决方案，将计算密集型任务卸载到边缘云服务器，相较于本地处理，可减少电池消耗。移动通信中即将实现的集成感知与通信技术有望改善轨迹预测和处理延迟。本研究针对多用户网络提出一种贪婪资源分配优化策略，以最小化总能耗。数值结果表明，每1000次迭代可能实现33%的性能提升。解决预测模型划分与速度精度问题对于获得更佳结果至关重要。本文为后续工作阶段概述了进一步改进和实现目标的计划。