Optimal sensor scheduling for remote state estimation with limited bandwidth: a deep reinforcement learning approach

Yang, Lixin; Rao, Hongxia; Lin, Ming; Xu, Yong; Shi, Peng

doi:10.1016/j.ins.2021.12.043

Cited by 15 publications

(4 citation statements)

References 33 publications

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“…and Yang et al [14] adopted deep Q-network (DQN), a data-driven deep reinforcement learning (DRL) algorithm, to solve similar scheduling problems in a relatively larger scale. More recently, Huang et al [15] developed an action-space reducing method of a multi-sensor-multi-actuator scheduling problem for enhancing the training efficiency.…”

Section: A Related Workmentioning

confidence: 99%

“…4) Oversimplified channel and inaccurate packet dropout models. For ease of tractability, most existing works on remote estimation considered static channels [14] or simplified binarystate (i.e., on-off) Markov channels [12], and assumed channel conditions of different sensors are identical [12], [14], [22]. In addition, the packet error rates (i.e., reliability) were calculated assuming either a coding-free scheme or an infinite blocklength coding scheme, and were approximated using symbol error rate [20] or Shannon capacity based formulas [17], [19],…”

Section: B Limitations and Challengesmentioning

confidence: 99%

“…Then, the average estimation MSE in the infinite time horizon can be converted as the average sum MSE per cycle. By following the similar analytical steps, we first derive an upper bound of the average sum MSE per cycle, and then we can show that the average sum MSE per cycle is bounded once (14) holds.…”

Section: Appendix: Proof Of Propositionmentioning

confidence: 99%

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DRL-based Resource Allocation in Remote State Estimation

Pang¹,

Liu²,

Li³

et al. 2022

Preprint

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Remote state estimation, where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multipleaccess and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants' parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.

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Section: A Related Workmentioning

confidence: 99%

Section: B Limitations and Challengesmentioning

confidence: 99%

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DRL-based Resource Allocation in Remote State Estimation

Pang¹,

Liu²,

Li³

et al. 2022

Preprint

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“…Zhang et al 12 proposed a distributed bearing-based formation control scheme to extend the application domain. Yang et al 13 adopted a scheduler to determine which systems to be observed and which sensors to access the network. Rafatnia and Mirzaei 14 proposed a new method for stability controller design of vehicle state estimation.…”

Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

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