| Autor: |
Shisher, Md Kamran Chowdhury, Ji, Bo, Hou, I-Hong, Sun, Yin |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 524-538, 2023 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/JSAIT.2023.3322620 |
| Popis: |
In this paper, we consider a remote inference system, where a neural network is used to infer a time-varying target (e.g., robot movement), based on features (e.g., video clips) that are progressively received from a sensing node (e.g., a camera). Each feature is a temporal sequence of sensory data. The inference error is determined by (i) the timeliness and (ii) the sequence length of the feature, where we use Age of Information (AoI) as a metric for timeliness. While a longer feature can typically provide better inference performance, it often requires more channel resources for sending the feature. To minimize the time-averaged inference error, we study a learning and communication co-design problem that jointly optimizes feature length selection and transmission scheduling. When there is a single sensor-predictor pair and a single channel, we develop low-complexity optimal co-designs for both the cases of time-invariant and time-variant feature length. When there are multiple sensor-predictor pairs and multiple channels, the co-design problem becomes a restless multi-arm multi-action bandit problem that is PSPACE-hard. For this setting, we design a low-complexity algorithm to solve the problem. Trace-driven evaluations demonstrate the potential of these co-designs to reduce inference error by up to 10000 times. |
| Databáze: |
arXiv |
| Externí odkaz: |
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