.png){kind=icon}
返回
Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions
.png){kind=icon}
OA
AI
DOI：10.1109/TCOMM.2021.3066495.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Computer architecture
Deep learning
Task analysis
Optimization
Inference algorithms
Array signal processing
Resource management
Mobile edge computing
reconfigurable intelligent surface
receive beamforming
energy partition
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.3
论文数:
1.2W
被引数:
3.6W
机构
引用论文
.png){kind=icon}
分享收藏Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems支持无人机的无线移动边缘计算系统中的计算速率最大化OAAI
OAAI分享收藏Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach移动边缘计算的智能资源分配: 一种深度强化学习方法
PREAI
分享收藏
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks用于无线供电的移动边缘计算网络中在线计算卸载的深度强化学习OAAI
OAAI分享收藏Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication无线通信能效的可重构智能表面OAAI
OAAI分享收藏
查看更多内容