NDLI: Power-efficient dynamic quantization for multisensor HMM state estimation over fading channels

Content Provider	IEEE Xplore Digital Library
Author	Ghasemi, N. Subhrakanti Dey
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Melbourne, VIC (Ghasemi, N.; Subhrakanti Dey)
Abstract	In this paper, we address the problem of designing power efficient quantizers for state estimation of hidden Markov models using multiple sensors communicating to a fusion centre via error-prone randomly time-varying flat fading channels modelled by finite state Markov chains. Our objective is to minimize a tradeoff between the long term average of mean square estimation error and expected total power consumption. We formulate the problem as a stochastic control problem by using Markov decision processes. Under some mild assumption on the measurement noise at the sensors, the discretized action space (quantization thresholds and transmission power levels) version of the optimization problem forms a unichain Markov decision process for stationary policies. The solution to the discretized problem provides optimal quantization thresholds and power levels to be communicated back to the sensors via a feedback channel. Moreover, in order to improve the performance of the quantization system, we employ a gradient- free stochastic optimization technique to determine the optimal set of quantization thresholds from which optimal quantization levels are determined. The performance results for estimation error/total transmission power tradeoff are studied under various channel conditions and sensor measurement qualities.
Starting Page	1553
Ending Page	1558
File Size	872337
Page Count	6
File Format	PDF
ISBN	9781424416875
DOI	10.1109/ISCCSP.2008.4537474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-12
Publisher Place	Malta
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Estimation error Energy consumption Quantization Sensor fusion Noise measurement hidden Markov models Power measurement fading channels Stochastic resonance Markov decision processes Power control Hidden Markov models state estimation State estimation
Content Type	Text
Resource Type	Article

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