A blind particle filtering detector of signals transmitted over flat fading channels

A new particle filtering detector (PFD) is proposed for blind signal detection over flat Rayleigh fading channels whose model coefficients are unknown. The detector employs a hybrid importance function and a mixture Kalman filter. It also incorporates an auxiliary particle filtering strategy with a smoothing kernel in the resampling step. Further, by considering practical information of communication systems and the physical interpretation of the adopted second-order autoregressive (AR) channel model, a fully blind particle filtering implementation is developed. The structure of the proposed PFD can be easily adapted to other system requirements. Simulations are provided that demonstrate the performance of the new PFD.

[1]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[2]  S. Gupta,et al.  Recursive Ideal Observer Detection of Known M-ary Signals in Multiplicative and Additive Gaussian Noise , 1973, IEEE Trans. Commun..

[3]  Iain B. Collings,et al.  Joint MAP equalization and channel estimation for frequency-selective and frequency-flat fast-fading channels , 2001, IEEE Trans. Commun..

[4]  P. Djurić,et al.  Uniform random parameter generation of stable minimum-phase real ARMA (p,q) processes , 1997, IEEE Signal Processing Letters.

[5]  Rong Chen,et al.  Adaptive Bayesian multiuser detection for synchronous CDMA with Gaussian and impulsive noise , 2000, IEEE Trans. Signal Process..

[6]  Thomas Kailath,et al.  Correlation detection of signals perturbed by a random channel , 1960, IRE Trans. Inf. Theory.

[7]  Jun S. Liu,et al.  Mixture Kalman filters , 2000 .

[8]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[9]  Petar M. Djuric,et al.  Sequential Monte Carlo sampling detector for Rayleigh fast-fading channels , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[10]  Petar M. Djuric,et al.  Hybrid Monte Carlo — Recursive identification algorithms for blind detection over a Rayleigh fading channel , 2000, 2000 10th European Signal Processing Conference.

[11]  Arnaud Doucet,et al.  Particle filters for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..

[12]  Anders Ahlén,et al.  Tracking of time-varying mobile radio channels. II. A case study , 2002, IEEE Trans. Commun..

[13]  Gordon L. Stuber,et al.  Principles of Mobile Communication , 1996 .

[14]  Subbarayan Pasupathy,et al.  Innovations-based MLSE for Rayleigh fading channels , 1995, IEEE Trans. Commun..

[15]  Hossein Zamiri-Jafarian,et al.  Oversampled blind MLSDE receiver , 2000, 2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record.

[16]  Rong Chen,et al.  Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering , 2000, IEEE Trans. Inf. Theory.

[17]  Petar M. Djuric,et al.  A hybrid importance function for particle filtering , 2004, IEEE Signal Processing Letters.

[18]  John H. Lodge,et al.  Maximum likelihood sequence estimation of CPM signals transmitted over Rayleigh flat-fading channels , 1990, IEEE Trans. Commun..

[19]  Rong Chen,et al.  Delayed-pilot sampling for mixture Kalman filter with application in fading channels , 2002, IEEE Trans. Signal Process..

[20]  Etienne Perret,et al.  Sequential Parameter Estimation of Time-Varying Non-Gaussian Autoregressive Processes , 2002, EURASIP J. Adv. Signal Process..

[21]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[22]  C. Fragouli,et al.  Channel estimation and equalization in fading , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[23]  Simon J. Godsill,et al.  Monte Carlo filtering and smoothing with application to time-varying spectral estimation , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[24]  Peter Hsin-Yu Wu,et al.  Multiuser detection and channel estimation for flat Rayleigh fading mobile radio CDMA channels , 1998 .

[25]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[26]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[27]  John G. Proakis,et al.  Digital Communications , 1983 .

[28]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[29]  Simon J. Godsill,et al.  Fixed-lag smoothing using sequential importance sampling , 1999 .

[30]  Desmond P. Taylor,et al.  Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels , 1995, IEEE Trans. Commun..

[31]  C. Dou.  Particle Filtering for Demodulation in Fading Channels with Non-Gaussian Additive Noise. , 2001 .

[32]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[33]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[34]  Michael A. West,et al.  Combined Parameter and State Estimation in Simulation-Based Filtering , 2001, Sequential Monte Carlo Methods in Practice.

[35]  Heinrich Meyr,et al.  A systematic approach to carrier recovery and detection of digitally phase modulated signals of fading channels , 1989, IEEE Trans. Commun..

[36]  G. David Forney,et al.  Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference , 1972, IEEE Trans. Inf. Theory.

[37]  Christophe Andrieu,et al.  Sequential Monte Carlo Methods for Optimal Filtering , 2001, Sequential Monte Carlo Methods in Practice.