Status Update for Accurate Remote Estimation: Centralized and Decentralized Schemes

Jingzhou SUN
Yuxuan SUN
Sheng ZHOU
Zhisheng NIU

IEICE TRANSACTIONS on Communications   Vol.E105-B    No.2    pp.131-139
Publication Date: 2022/02/01
Publicized: 2021/08/17
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2021CEI0002
Type of Manuscript: Special Section INVITED PAPER (Special Section on Emerging Communication Technologies in Conjunction with Main Topics of ICETC2020)
remote estimation,  age of information,  decentralized access,  mean-field model,  

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In this work, we consider a remote estimation system where a remote controller estimates the status of heterogeneous sensing devices with the information delivered over wireless channels. Status of heterogeneous devices changes at different speeds. With limited wireless resources, estimating as accurately as possible requires careful design of status update schemes. Status update schemes can be divided into two classes: centralized and decentralized. In centralized schemes, a central scheduler coordinates devices to avoid potential collisions. However, in decentralized schemes where each device updates on its own, update decisions can be made by using the current status which is unavailable in centralized schemes. The relation between these two schemes under the heterogeneous devices case is unclear, and thus we study these two schemes in terms of the mean square error (MSE) of the estimation. For centralized schemes, since the scheduler does not have the current status of each device, we study policies where the scheduling decisions are based on age of information (AoI), which measures the staleness of the status information held in the controller. The optimal scheduling policy is provided, along with the corresponding MSE. For decentralized schemes, we consider deviation-based policies with which only devices with estimation deviations larger than prescribed thresholds may update, and the others stay idle. We derive an approximation of the minimum MSE under the deviation-based policies and show that it is e/3 of the minimum MSE under the AoI-based policies. Simulation results further show that the actual minimum MSEs of these two policies are even closer than that shown by the approximation, which indicates that the cost of collision in the deviation-based policy cancels out the gain from exploiting status deviations.