Low-complexity a posteriori probability approximation in EM-based channel estimation for trellis-coded systems

When estimating channel parameters in linearly modulated communication systems, the iterative expectation-maximization (EM) algorithm can be used to exploit the signal energy associated with the unknown data symbols. It turns out that the channel estimation requires at each EM iteration the a posteriori probabilities (APPs) of these data symbols, resulting in a high computational complexity when channel coding is present. In this paper, we present a new approximation of the APPs of trellis-coded symbols, which is less complex and requires less memory than alternatives from literature. By means of computer simulations, we show that the Viterbi decoder that uses the EM channel estimate resulting from this APP approximation experiences a negligible degradation in frame error rate (FER) performance, as compared to using the exact APPs in the channel estimation process.

[1]  Jinho Choi.  Adaptive and Iterative Signal Processing in Communications , 2006 .

[2]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

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

[4]  Jun-Hong Cui,et al.  Bandwidth management for guaranteeing transient loss performance of real-time communications , 1997, GLOBECOM 97. IEEE Global Telecommunications Conference. Conference Record.

[5]  L. Lee.  Real-Time Minimal-Bit-Error Probability Decoding of Convolutional Codes , 1974, IEEE Trans. Commun..

[6]  Ahmed A. Hamad.  Performance Enhancement of SOVA Based Decoder in SCCC and PCCC Schemes , 2013 .

[7]  Barry G. Evans,et al.  Improved performance SOVA turbo decoder , 2006 .

[8]  L. Litwin,et al.  Error control coding , 2001 .

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

[10]  Ravi Motwani,et al.  Reduced-Complexity Soft-Output Viterbi Algorithm for Channels Characterized by Dominant Error Events , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[11]  Joachim Hagenauer,et al.  A Viterbi algorithm with soft-decision outputs and its applications , 1989, IEEE Global Telecommunications Conference, 1989, and Exhibition. 'Communications Technology for the 1990s and Beyond.

[12]  Marc Moeneclaey,et al.  SAGE-based estimation algorithms for time-varying channels in Amplify-and-forward cooperative networks , 2012, 2012 International Symposium on Information Theory and its Applications.

[13]  Ali Ghrayeb,et al.  A simple remedy for the exaggerated extrinsic information produced by the SOVA algorithm , 2006, IEEE Transactions on Wireless Communications.

[14]  Heinrich Meyr,et al.  Soft output M-algorithm equalizer and trellis-coded modulation for mobile radio communication , 1992, [1992 Proceedings] Vehicular Technology Society 42nd VTS Conference - Frontiers of Technology.

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  Umberto Mengali,et al.  The modified Cramer-Rao bound and its application to synchronization problems , 1994, IEEE Trans. Commun..

[17]  M.R. Shikh-Bahaei,et al.  Interference cancellation in W-CDMA cellular structures using statistical processing , 1999, Seamless Interconnection for Universal Services. Global Telecommunications Conference. GLOBECOM'99. (Cat. No.99CH37042).

[18]  Christos Masouros,et al.  IEEE Global Telecommunications Conference , 2015 .

[19]  Ha H. Nguyen,et al.  Unified scaling factor approach for turbo decoding algorithms , 2010, IET Commun..

[20]  Sriram Vishwanath,et al.  Complexity based design for iterative joint equalization and decoding , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).