标题： 多任务DNN推理在MEC辅助的XR设备中的能量优化：一种Lyapunov引导的强化学习方法 显示英文标题

标题： Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach

摘要： 扩展现实（XR），融合虚拟和现实世界，是未来网络的关键应用。 虽然人工智能的进步增强了XR的能力，但也对轻量级XR设备提出了显著的计算和能源挑战。 在本文中，我们开发了一个用于多任务深度神经网络推理的分布式队列模型，解决了资源竞争和队列耦合的问题。 为应对XR设备高能耗和资源有限的挑战，我们设计了一个双时间尺度联合优化策略，用于模型分割和资源分配，将其建模为一个双层优化问题。 该策略旨在最小化XR设备的总能耗，同时确保队列稳定，并遵守计算和通信资源约束。 为了解决这个问题，我们设计了一个基于李雅普诺夫的近端策略优化算法，命名为LyaPPO。 数值结果表明，LyaPPO算法优于基线方法，在不同资源容量下实现了24.79%至46.14%的节能效果。 具体而言，与基线算法相比，所提出的算法将XR设备的能耗降低了24.29%至56.62%。 显示英文摘要

摘要： Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided Proximal Policy Optimization algorithm, named LyaPPO. Numerical results demonstrate that the LyaPPO algorithm outperforms the baselines, achieving energy conservation of 24.79% to 46.14% under varying resource capacities. Specifically, the proposed algorithm reduces the energy consumption of XR devices by 24.29% to 56.62% compared to baseline algorithms.