Communication Theory Vector quantisation for finite-state Markov channels and application to wireless communications

Vector quantisation for joint source-channel (JSC) coding over a finite-state Markov channel (FSMC) is studied. In particular, minimum mean square error (MMSE) decoding of a vector quantised source in the absence of channel state information (CSI) is considered. Based on hidden Markov modelling of the channel output, two decoding strategies are proposed. The first one is a soft-decoder which estimates the source reconstruction vectors directly from a sequence of channel output samples. The second one is a hard-decoder based on joint maximum a posteriori (MAP) probability estimation of channel symbols and channel states. An iterative procedure for designing JSC optimised vector quantisers (VQs) is also proposed. Finally, the design of VQs for wireless channels using a finite-state model is examined. Experimental results are provided for a Gauss-Markov source and flat-fading wireless channels. A comparison with an idealised tandem source-channel coding system is also provided to demonstrate the advantage of the proposed JSC coding approach. Copyright © 2007 John Wiley & Sons, Ltd.

[1]  Vladimir Cuperman,et al.  Joint source and channel coding using a non-linear receiver over Rayleigh fading channel , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

[2]  Mikael Skoglund,et al.  Soft Decoding for Vector Quantization Over Noisy Channels with Memory , 1999, IEEE Trans. Inf. Theory.

[3]  Vladimir Cuperman,et al.  Sequential reconstruction of vector quantized signals transmitted over Rayleigh fading channels , 1994, Proceedings of ICC/SUPERCOMM'94 - 1994 International Conference on Communications.

[4]  Nariman Farvardin,et al.  A study of vector quantization for noisy channels , 1990, IEEE Trans. Inf. Theory.

[5]  H. Kong,et al.  Sequence detection and channel state estimation over finite state Markov channels , 1999 .

[6]  Nam C. Phamdo,et al.  A unified approach to tree-structured and multistage vector quantization for noisy channels , 1993, IEEE Trans. Inf. Theory.

[7]  Robert E. Van Dyck.  MPEG-4 image transmission using MAP source-controlled channel decoding , 2000, IEEE J. Sel. Areas Commun..

[8]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[9]  Fady Alajaji,et al.  Soft-decision COVQ for turbo-coded AWGN and Rayleigh fading channels , 2001, IEEE Communications Letters.

[10]  Allen Gersho,et al.  Variable Rate Vector Quantization , 1992 .

[11]  P. Billingsley,et al.  Statistical Methods in Markov Chains , 1961 .

[12]  Venceslav Kafedziski,et al.  Vector quantization over Gaussian channels with memory , 1995, Proceedings IEEE International Conference on Communications ICC '95.

[13]  Wayne E. Stark,et al.  Channels with block interference , 1984, IEEE Trans. Inf. Theory.

[14]  D. Verdin,et al.  Generating a fading process for the simulation of land-mobile radio communications , 1993 .

[15]  Amir K. Khandani,et al.  Efficient source decoding over memoryless noisy channels using higher order Markov models , 2004, IEEE Transactions on Information Theory.

[16]  Mikael Skoglund.  A soft decoder vector quantizer for a Rayleigh fading channel: application to image transmission , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

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

[18]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[19]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[20]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[21]  Vladimir Cuperman,et al.  Joint source and channel coding using a non-linear receiver , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[22]  David J. Miller,et al.  Transport of wireless video using separate, concatenated, and joint source-channel coding , 1999, Proc. IEEE.

[23]  Michelle Effros.  Robustness to channel variation in source coding for transmission across noisy channels , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[24]  Kiyotaka Yamamura,et al.  DECOMPOSITION METHOD AND ACCELERATION TECHNIQUES FOR A FIXED POINT ALGORITHM. , 1984 .

[25]  Norman C. Beaulieu,et al.  On first-order Markov modeling for the Rayleigh fading channel , 2000, IEEE Trans. Commun..

[26]  Richard D. Wesel,et al.  Joint iterative channel estimation and decoding in flat correlated Rayleigh fading , 2001, IEEE J. Sel. Areas Commun..

[27]  M. Salehi,et al.  Optimal quantization for finite state channels , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[28]  William Turin,et al.  Hidden Markov modeling of flat fading channels , 1998, IEEE J. Sel. Areas Commun..

[29]  Mikael Skoglund,et al.  On nonlinear utilization of intervector dependency in vector quantization , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[30]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[31]  Pradeepa Yahampath,et al.  On finite-state vector quantization for noisy channels , 2004, IEEE Transactions on Communications.

[32]  David J. Miller,et al.  A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models , 1998, IEEE Trans. Commun..

[33]  Fady Alajaji,et al.  Quantization of memoryless and Gauss-Markov sources over binary Markov channels , 1997, IEEE Trans. Commun..

[34]  Masao Kasahara,et al.  A construction of vector quantizers for noisy channels , 1984 .

[35]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

[36]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .