Paper Info
Title
Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems
Abstract
In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N<sup>3</sup>) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented
Year
DOI
Venue
1998
10.1109/KES.1998.725840
KES
Keywords
DocType
Volume
iterative methods,additive white gaussian noise,hidden markov models,state space,iterative method,awgn,kalman filters,matrix representation,demodulation,fading,hidden markov model,fading channel,kalman filter
Conference
1
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
Jason Ford111.39
JOHN B. MOORE241284.61