2022/08/15 by Md Kamran Chowdhury Shisher, Yin Sun, Shisher, Md Kamran Chowdhury +1 · 7 citations
Computer Science · Mathematics · #Age of Information Optimization #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #cs.IT #cs.LG #cs.NI #math.IT
paper · pdf · doi:10.48550/arxiv.2208.06948
21 Pages, 12 figures, Part of this work has been accepted by ACM MobiHoc, 2022
openalex publication_date 2022/08/15 · arxiv created 2022/09/23 · arxiv updated 2022/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we analyze the impact of data freshness on real-time supervised learning, where a neural network is trained to infer a time-varying target (e.g., the position of the vehicle in front) based on features (e.g., video frames) observed at a sensing node (e.g., camera or lidar). One might expect that the performance of real-time supervised learning degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain; it is not true if the data sequence is far from Markovian. Hence, the prediction error of real-time supervised learning is a function of the Age of Information (AoI), where the function could be non-monotonic. Several experiments are conducted to illustrate the monotonic and non-monotonic behaviors of the prediction error. To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. By using Gittins and Whittle indices, low-complexity scheduling strategies are developed to minimize the inference error, where a new connection between the Gittins index theory and Age of Information (AoI) minimization is discovered. These scheduling results hold (i) for minimizing general AoI functions (monotonic or non-monotonic) and (ii) for general feature transmission time distributions. Data-driven evaluations are presented to illustrate the benefits of the proposed scheduling algorithms.