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Reinforcement-Learning-Based Offloading for RIS-Aided Cloud-Edge Computing in IoT Networks: Modeling, Analysis, and Optimization
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DOI：10.1109/JIOT.2024.3367791.png)
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Abstract
En 中文
The rapid advancement of wireless communication and artificial intelligence (AI) has led to a plethora of emerging applications that require exceptional connectivity, minimal latency, and substantial computing resources. The widespread adoption of cloud-edge intelligence is propelling the development of future networks capable of supporting intelligent computing. Mobile-edge computing (MEC) technology facilitates the movement of computing resources and storage to the network's edge, enabling cost-effective offloading of computational tasks for related applications which needs for reduced latency and improved energy efficiency. However, the offloading efficiency is hindered by limitations of wireless transmission capacity. This article aims to address this issue by integrating reconfigurable intelligent surfaces (RISs) into a cell-free network within an intelligent cloud-edge system. The core idea is to strategically deploy passive RISs around base stations (BSs) to reconstruct the transmission channel and improve the corresponding capacity. Subsequently, we formulate an optimal problem involving joint beamforming for RISs and BSs, which is characterized by nonconvexity and complexity. To tackle this challenge, we employ an alternating optimization scheme to ensure the effectiveness of joint beamforming. In particular, deep reinforcement learning (DRL) is leveraged to reduce the computational complexity involved in optimizing task offloading. Additionally, Lyapunov optimization is utilized to model the latency queue and improve the learning efficiency of the offloading framework. We conduct comprehensive evaluations on the wireless system's capacity, average latency, and energy consumption, considering the integration of RIS with the DRL offloading framework. Experimental results demonstrate that our proposed scheme achieves superior efficiency and robustness.
Keywords:
Wireless communication
Task analysis
Optimization
Reconfigurable intelligent surfaces
Cloud computing
Internet of Things
Energy consumption
Cloud-edge offloading
reconfigurable intelligent surface (RIS)
reinforcement learning
resource allocation
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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