Timely Communications for Remote Inference
arxiv(2024)
摘要
In this paper, we analyze the impact of data freshness on remote inference
systems, where a pre-trained neural network infers a time-varying target (e.g.,
the locations of vehicles and pedestrians) based on features (e.g., video
frames) observed at a sensing node (e.g., a camera). One might expect that the
performance of a remote inference system 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, whereas it is not true if the data sequence is far from
Markovian. Hence, the inference error is a function of Age of Information
(AoI), where the function could be non-monotonic. 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. In addition, we design low-complexity scheduling policies to
improve inference performance. For single-source, single-channel systems, we
provide an optimal scheduling policy. In multi-source, multi-channel systems,
the scheduling problem becomes a multi-action restless multi-armed bandit
problem. For this setting, we design a new scheduling policy by integrating
Whittle index-based source selection and duality-based feature
selection-from-buffer algorithms. This new scheduling policy is proven to be
asymptotically optimal. These scheduling results hold for minimizing general
AoI functions (monotonic or non-monotonic). Data-driven evaluations demonstrate
the significant advantages of our proposed scheduling policies.
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