Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929396021985280 |
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| author | Shisher, Md Kamran Chowdhury Ji, Bo Hou, I-Hong Sun, Yin |
| author_facet | Shisher, Md Kamran Chowdhury Ji, Bo Hou, I-Hong Sun, Yin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_10094 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling Shisher, Md Kamran Chowdhury Ji, Bo Hou, I-Hong Sun, Yin Information Theory Networking and Internet Architecture 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. |
| title | Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling |
| topic | Information Theory Networking and Internet Architecture |
| url | https://arxiv.org/abs/2308.10094 |