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arXiv 2102.07384eess.SP
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可重构智能表面辅助移动边缘计算:从基于优化到基于位置学习的解决方案

Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions

Xiaoyan Hu, Christos Masouros, Kai-Kit Wong

摘要:

在本文中,我们探索基于优化和数据驱动的解决方案,在可重构智能表面(RIS)辅助多用户移动边缘计算(MEC)系统,其中用户设备(UE)可以部分卸载其计算任务的接入点(AP)。我们的目标是最大限度地提高总完成的任务输入比特(TCTB)的所有UE在一个给定的时隙有限的能量预算,通过联合优化RIS反射系数,AP的接收波束成形向量,和UE的能量分区策略,本地计算和卸载。首先提出了一种三步块坐标下降(BCD)算法,有效地解决了非凸TCTB最大化问题,并保证收敛。为了降低优化算法的计算复杂度和便于轻量级在线实现,我们进一步构建了两种深度学习架构。第一种方法将信道状态信息(CSI)作为输入,而第二种方法仅利用UE的位置进行在线推断。这两种数据驱动的方法是使用BCD算法通过监督学习生成的数据样本进行训练的。我们的仿真结果显示,基于优化的BCD算法和低复杂度的基于学习的架构的性能之间的密切匹配,所有具有优越的性能,现有的计划在这两种情况下,完美和不完美的输入功能。重要的是,仅位置深度学习方法被证明提供了一种特别实用和鲁棒的解决方案,当UE和AP之间存在视距(LoS)直接链路时,可以减轻对CSI估计和反馈的需求。

英文摘要:

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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