Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State
- 16 April 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 60 (7) , 3834-3844
Abstract
Starting from the definition of mutual information, one promptly realizes that the probabilities inferred by Bayesian tracking can be used to compute the Shannon information between the state and the measurement of a dynamic system. In the Gaussian and linear case, the information rate can be evaluated from the probabilities computed by the Kalman filter. When the probability distributions inferred by Bayesian tracking are nontractable, one is forced to resort to approximated inference, which gives only an approximation to the wanted probabilities. We propose upper and lower bounds to the information rate between the hidden state and the measurement based on approximated inference. Application of these bounds to multiplicative communication channels is discussed, and experimental results for the discrete-time phase noise channel and for the Gauss-Markov fading channel are presented.Keywords
Funding Information
- Technische Universität München-Institute for Advanced Study through the German Excellence Initiative
This publication has 30 references indexed in Scilit:
- On continuous-time white phase noise channels2014
- On Wiener phase noise channels at high Signal-to-Noise Ratio2013
- Bridging the Gap Between Kalman Filter and Wiener Filter in Carrier Phase TrackingIEEE Photonics Technology Letters, 2013
- The Information Rate Transferred Through the Discrete-Time Wiener's Phase Noise ChannelJournal of Lightwave Technology, 2012
- On the Information Rate and Repeat-Accumulate Code Design for Phase Noise ChannelsIEEE Transactions on Communications, 2011
- Calculation of Mutual Information for Partially Coherent Gaussian Channels With Applications to Fiber OpticsIEEE Transactions on Information Theory, 2011
- Estimate of Information Rates of Discrete-Time First-Order Markov Phase Noise ChannelsIEEE Photonics Technology Letters, 2011
- Computation of Information Rates by Particle MethodsIEEE Transactions on Information Theory, 2008
- On phase noise channels at high SNR2003
- Shaping gain of the partially coherent additive white Gaussian noise channelIEEE Communications Letters, 2002