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

Muhammad U. Ilyas, Hayder Radha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication2009 43rd Annual Conference on Information Sciences and Systems
PublisherIEEE
Pages241-246
Number of pages6
ISBN (Print)978-1-4244-2734-5
DOIs
Publication statusPublished - 20 Mar 2009
Event2009 43rd Annual Conference on Information Sciences and Systems - Baltimore, MD, USA
Duration: 18 Mar 200920 Mar 2009

Conference

Conference2009 43rd Annual Conference on Information Sciences and Systems
Period18/03/0920/03/09

Keywords

  • Hidden Markov models
  • Predictive models
  • Bit error rate
  • Wireless sensor networks
  • Semiconductor device measurement
  • Physical layer
  • Cyclic redundancy check
  • State estimation
  • Stochastic processes
  • Training data

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