NDLI: Modeling, estimating and predicting the packet-level Bit Error Rate process in IEEE 802.15.4 LR-WPANs using Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Ilyas, M.U. Radha, H.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824-1226, USA (Ilyas, M.U.; Radha, H.)
Abstract	This paper describes a stochastic wireless channel model that captures the behavior of the packet-level Bit Error Rate (BER) and the Link Quality Indication (LQI) processes. The model is based on a discrete-time Hidden Markov Model (HMM) whose hidden states correspond to different BERs, and whose observable states correspond to different LQI values. We use the Baum-Welch algorithm to train the HMM. The data required as input to the training phase is captured experimentally using IEEE 802.15.4 compliant Crossbow MICAz motes. We demonstrate the HMM-based Channel Model's (HCM) utility and versatility by three applications. In the first we use it to synthesize traces whose properties closely resemble those of the training data. This application simultaneously demonstrates the HCM's correctness as well. In the second application we demonstrate the HCM's ability to estimate a received packet's BER based on the LQI with which it was received. In the third application we demonstrate the HCM's ability to predict the BER to which future packets will be subjected. For evaluation purpose we restrict our prediction to the next packet.
Starting Page	241
Ending Page	246
File Size	180198
Page Count	6
File Format	PDF
ISBN	9781424427338
DOI	10.1109/CISS.2009.5054724
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cyclic redundancy check Wireless sensor networks Semiconductor device measurement Bit error rate Hidden Markov models Stochastic processes Training data Predictive models Physical layer State estimation
Content Type	Text
Resource Type	Article

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