Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1810.09820 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909560771444736 |
|---|---|
| author | Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling |
| author_facet | Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling |
| contents | We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming the channel statistics is known. We prove that the Q-factor is monotonic and submodular, which leads to the threshold-like structures in both types of problems. Then we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We prove the convergence of these algorithms. Performance improvement compared with the standard Q-learning algorithm is shown through numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1810_09820 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling Systems and Control We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming the channel statistics is known. We prove that the Q-factor is monotonic and submodular, which leads to the threshold-like structures in both types of problems. Then we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We prove the convergence of these algorithms. Performance improvement compared with the standard Q-learning algorithm is shown through numerical examples. |
| title | Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition |
| topic | Systems and Control |
| url | https://arxiv.org/abs/1810.09820 |