AA-DL: AoI-Aware Deep Learning Approach for D2D-Assisted Industrial IoT.
CoRR(2023)
摘要
In real-time Industrial Internet of Things (IIoT), e.g., monitoring and
control scenarios, the freshness of data is crucial to maintain the system
functionality and stability. In this paper, we propose an AoI-Aware Deep
Learning (AA-DL) approach to minimize the Peak Age of Information (PAoI) in
D2D-assisted IIoT networks. Particularly, we analyzed the success probability
and the average PAoI via stochastic geometry, and formulate an optimization
problem with the objective to find the optimal scheduling policy that minimizes
PAoI. In order to solve the non-convex scheduling problem, we develop a Neural
Network (NN) structure that exploits the Geographic Location Information (GLI)
along with feedback stages to perform unsupervised learning over randomly
deployed networks. Our motivation is based on the observation that in various
transmission contexts, the wireless channel intensity is mainly influenced by
distancedependant path loss, which could be calculated using the GLI of each
link. The performance of the AA-DL method is evaluated via numerical results
that demonstrate the effectiveness of our proposed method to improve the PAoI
performance compared to a recent benchmark while maintains lower complexity
against the conventional iterative optimization method.
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关键词
Industrial IoT,neural networks,age of information
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