Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks

We develop a new and efficient algorithm to solve the problem of joint channel and Doppler offset estimation in time-varying cooperative wireless relay networks. We first formulate the problem as a Bayesian dynamic nonlinear state space model, then develop an algorithm, which is based on particle adaptive marginal Markov chain Monte Carlo, method to jointly estimate the time-varying channels and static Doppler offsets. We perform detailed complexity analysis of the proposed algorithm and show that it is very efficient and requires moderate computational complexity. In addition, we develop a new version of the recursive marginal Cramér-Rao lower bound and derive expressions for the achievable mean-square error. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms and performs close to the Cramér-Rao lower bound.

[1]  T. Ghirmai.  Sequential Monte Carlo Method for Fixed Symbol Timing Estimation and Data Detection , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[2]  Gareth O. Roberts,et al.  Examples of Adaptive MCMC , 2009 .

[3]  J. Rosenthal,et al.  Optimal scaling for various Metropolis-Hastings algorithms , 2001 .

[4]  John B. Nicholas,et al.  Exploiting Distributed Spatial Diversity in Wireless Networks , 2000 .

[5]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[6]  E. Meulen,et al.  Three-terminal communication channels , 1971, Advances in Applied Probability.

[7]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[8]  Gregory W. Wornell,et al.  Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks , 2003, IEEE Trans. Inf. Theory.

[9]  Eric Pierre Simon,et al.  Joint Carrier Frequency Offset and Channel Estimation for OFDM Systems via the EM Algorithm in the Presence of Very High Mobility , 2012, IEEE Transactions on Signal Processing.

[10]  Gordon L. Stüber,et al.  Simulation of Rayleigh-faded mobile-to-mobile communication channels , 2005, IEEE Transactions on Communications.

[11]  Feifei Gao,et al.  On channel estimation and optimal training design for amplify and forward relay networks , 2008, IEEE Transactions on Wireless Communications.

[12]  Tung-Sang Ng,et al.  Semiblind Iterative Data Detection for OFDM Systems with CFO and Doubly Selective Channels , 2010, IEEE Transactions on Communications.

[13]  Tung-Sang Ng,et al.  Superimposed Training-Based Channel Estimation and Data Detection for OFDM Amplify-and-Forward Cooperative Systems Under High Mobility , 2012, IEEE Transactions on Signal Processing.

[14]  Ali A. Nasir,et al.  Mixture Kalman filtering for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Xiangyun Zhou,et al.  Kalman filter-based channel estimation for amplify and forward relay communications , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[16]  Ronald A. Iltis,et al.  Joint Carrier Frequency Offset and Channel Estimation for Uplink MIMO-OFDMA Systems Using Parallel Schmidt Rao-Blackwellized Particle Filters , 2010, IEEE Transactions on Communications.

[17]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[18]  Aria Nosratinia,et al.  Cooperative communication in wireless networks , 2004, IEEE Communications Magazine.

[19]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[20]  P. Moral.  Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications , 2004 .

[21]  Hong Shen Wang,et al.  On verifying the first-order Markovian assumption for a Rayleigh fading channel model , 1994, Proceedings of 1994 3rd IEEE International Conference on Universal Personal Communications.

[22]  Laurent Ros,et al.  On the use of first-order autoregressive modeling for Rayleigh flat fading channel estimation with Kalman filter , 2012, Signal Process..

[23]  M. Xiong,et al.  Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks , 2008, PloS one.

[24]  François Septier,et al.  MCMC sampling for joint estimation of phase distortions and transmitted symbols in OFDM systems , 2011, Digit. Signal Process..

[25]  Ahmed M. Eltawil,et al.  On Channel Estimation and Capacity for Amplify and Forward Relay Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[26]  Gregory W. Wornell,et al.  Cooperative diversity in wireless networks: Efficient protocols and outage behavior , 2004, IEEE Transactions on Information Theory.

[27]  H. Vincent Poor,et al.  Frequency Offset and Channel Estimation in Cooperative Relay Networks , 2011, IEEE Transactions on Vehicular Technology.