Optimal Periodic Transmission Power Schedules for Remote Estimation of ARMA Processes

We investigate sensor transmission power control for remote state estimation. Instead of using a conventional sensor, a sensor equipped with an energy harvester which can obtain energy from the external environment is utilized. We formulate power control of the energy harvesting sensor into an infinite time-horizon Markov decision process (MDP). To deal with the computation complexity associated with this multi-dimensional MDP, a continuous-time approach and perturbation analysis are used and a closed-form approximate value function is derived. Based on the approximation, we obtain a closed-form optimal power control solution which has a threshold-based structure. A numerical example is provided to evaluate the estimation performance of the optimal solution compared with other power scheduling schemes.

show abstract

“…Based on the analysis in [18], the approximate relationship between the symbol error rate (SER) and ω k is given by:…”

Section: B Wireless Communication Modelmentioning

confidence: 99%

Power Control of an Energy Harvesting Sensor for Remote State Estimation

Zhang

Quevedo

et al. 2017

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We suppose that the AWGN channel uses the well-established quantized quadrature amplitude modulation (QAM) mechanism [10]. For an AWGN channel with quantized QAM, if the sensor measurement is quantized into R bits and mapped to one of the 2 R available QAM symbols, the symbolic error rate is given by [11] …”

Section: Packet Dropout Vs Transmission Energymentioning

confidence: 99%

Stabilization of NCSs by random allocation of transmission power to sensors

Wang

Guo

Zhuang

2016

Sci. China Inf. Sci.

View full text Add to dashboard Cite

This study investigates networked control systems (NCSs), whose sensors communicate with remote controllers via a wireless fading channel. The sensor can choose different power levels at which it can transmit its measurement to the controller. The transmission power is selected according to a given probability distribution. The level of transmission power determines the probability of packet loss. The objective of this study is to find an appropriate transmission power probability distribution and a system controller jointly such that NCSs can be exponentially stabilized within a given energy budget. By the average dwell time technique, sufficient conditions for almost sure stability and an optimal sensor power probability distribution maximizing the stability margin are obtained. The effectiveness of the results is demonstrated by numerical simulations.

show abstract

“…As an example, the interested readers are referred to [9] to see the success transmission model over an additive white Gaussian noise (AWGN) channel using quadrature amplitude modulation (QAM). We consider the communication environment where g m (k) is a first-order stationary and homogeneous Markovian process.…”

Section: A Communication Modelmentioning

confidence: 99%

“…Consider the problem of detecting a mean-shift in Gaussian noises with three identical sensors, specifically, f m 0 ∼ N (0, 1) and f m 1 ∼ N (1, 1), ∀m ∈ {1, 2, 3}. AWGN channel using QAM similar to [9] is adopted, where the packet dropout probabilities has the following form:…”

Section: Examplesmentioning

confidence: 99%

Bayesian quickest change detection under energy constraints over wireless sensor networks with correlated fading channels

Ren

Shi

2014

11th IEEE International Conference on Control &Amp; Automation (ICCA)

Self Cite

View full text Add to dashboard Cite

In the existing approaches to Bayesian quickest change detection under energy constraints, the consumed energy is formulated as the number of observations and the communication channels between the sensors and the fusion center are assumed to be perfect, which are not quite realistic. We consider the correlated wireless fading channels and the associated packet loss probabilities depend on both time-varying channel gains and the power levels being used by the sensors. The channel gains are governed by a first-order stationary and homogeneous Markovian process and the power level can be adjusted by the fusion center. The optimal power control and stopping rules are studied to minimize the average detection delay as well as to satisfy certain energy constraints. This optimization problem is solved by formulating it as a partially observable Markov decision process (POMDP) and the optimal stopping rules are shown to have "weak" threshold structure. A numerical example is given to illustrate the main results. I. INTROCUTIONThe central Bayesian quickest change detection problem was proposed by Shiryaev [1], in which the change time is assumed to have the a priori geometric distribution, a sequence of observations are taken and the goal is to find the optimal stopping rule to minimize the average delay subject to certain false alarm rate. The decentralized counterpart is studied in [2], [3] and some useful results about quantization policy at the side of sensors are obtained.The above literature, however, did not consider energy constraints, which is of ultimate importance in applications using wireless sensor networks (WSNs). This is because the sensors are usually battery-powered and have limited energy. Recently, the problem of quickest change detection with energy constraints are studied in [4]-[7]. The energy consumed in these papers is formulated as the number of observations taken and the sensors are switched off when necessary to conserve energy. The optimal sleep/awake and stopping rules are studied.The limit of the above models, however, is that the authors assumed the communication channels between the sensors and the fusion center are perfect and each observation consumes equal energy, which is not quite realistic in WSNs.The authors are with the This work is supported by an HKUST Caltech Partnership FP004.Packet dropout is common in wireless communications due to many factors, such as fading channels, interference and low signal-to-noise ratio (SNR). In this paper, we consider the scenario where the data sent from the sensor nodes to the fusion center may get dropped over the fading channels. Packet dropout probabilities depend on both power levels of sensors and time-varying channel gains. The channel gains may be correlated with each other (see details in Section II-A). At each time slot, the fusion center makes a global decision based on available observations collected from the sensors and the state of communication channels to stop and declare the change or to continue. If continues, the center needs ...

show abstract

Optimal Periodic Transmission Power Schedules for Remote Estimation of ARMA Processes

Cited by 58 publications

References 32 publications

Power Control of an Energy Harvesting Sensor for Remote State Estimation

Power Control of an Energy Harvesting Sensor for Remote State Estimation

Stabilization of NCSs by random allocation of transmission power to sensors

Bayesian quickest change detection under energy constraints over wireless sensor networks with correlated fading channels

Contact Info

Product

Resources

About