Blind Decoding of Multiple Description Codes over OFDM Systems via Sequential Monte Carlo

We consider the problem of transmitting a continuous source through an OFDM system. Multiple description scalar quantization (MDSQ) is applied to the source signal, resulting in two correlated source descriptions. The two descriptions are then OFDM modulated and transmitted through two parallel frequency-selective fading channels. At the receiver, a blind turbo receiver is developed for joint OFDM demodulation and MDSQ decoding. Transformation of the extrinsic information of the two descriptions are exchanged between each other to improve system performance. A blind soft-input soft-output OFDM detector is developed, which is based on the techniques of importance sampling and resampling. Such a detector is capable of exchanging the so-called extrinsic information with the other component in the above turbo receiver, and successively improving the overall receiver performance. Finally, we also treat channel-coded systems, and a novel blind turbo receiver is developed for joint demodulation, channel decoding, and MDSQ source decoding.

[1]  Norbert Goertz,et al.  Turbo cross decoding of multiple descriptions , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[2]  Vinay A. Vaishampayan,et al.  Design of multiple description scalar quantizers , 1993, IEEE Trans. Inf. Theory.

[3]  Nando de Freitas,et al.  Sequential Monte Carlo in Practice , 2001 .

[4]  Kannan Ramchandran,et al.  Wireless image transmission using multiple-description based concatenated codes , 2000, Proceedings DCC 2000. Data Compression Conference.

[5]  Xiaodong Wang,et al.  A sequential Monte Carlo blind receiver for OFDM systems in frequency-selective fading channels , 2002, IEEE Trans. Signal Process..

[6]  Yucel Altunbasak,et al.  Multiple description coding with multiple transmit and receive antennas for wireless channels: the case of digital modulation , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

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

[8]  John B. Anderson,et al.  Mismatched Decoding of Intersymbol Interference Using a Parallel Concatenated Scheme , 1998, IEEE J. Sel. Areas Commun..

[9]  Rong Chen,et al.  Monte Carlo Bayesian Signal Processing for Wireless Communications , 2002, J. VLSI Signal Process..

[10]  L. Ozarow,et al.  On a source-coding problem with two channels and three receivers , 1980, The Bell System Technical Journal.

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

[12]  Abbas El Gamal,et al.  Achievable rates for multiple descriptions , 1982, IEEE Trans. Inf. Theory.

[13]  Mehul Motani,et al.  Wireless video transmission using multiple description codes combined with prioritized DCT compression , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[14]  Vinay A. Vaishampayan,et al.  Low-delay communication for Rayleigh fading channels: an application of the multiple description quantizer , 1995, IEEE Trans. Commun..