Recursive LLR Combining in Iterative Multiuser Decoding of Coded CDMA

Due to the prohibitive complexity of optimal multiuser detection, suboptimal structures are commonly suggested for use in iterative multiuser decoders. The use of suboptimal components in the iterative structure leads to performance losses in terms of system load, bit error rate (BER), and convergence speed. In this paper, we introduce a simple recursive filtering structure into the iterative multiuser decoder, aimed at improving the performance. Optimal filter coefficients are determined based on the joint likelihood function of the correlated output loglikelihood ratios (LLRs) over iterations. Numerical results show that iterative multiuser systems with recursive LLR filtering and a posteriori feedback at the single-user decoder stage can achieve superior BER performance, produce convergence in fewer iterations, and accommodate an increasing number of active users.

[1]  J. Boutros,et al.  Iterative multiuser joint decoding: unified framework and asymptotic analysis , 2001, Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252).

[2]  Michael Moher,et al.  An iterative multiuser decoder for near-capacity communications , 1998, IEEE Trans. Commun..

[3]  Lars K. Rasmussen,et al.  Iterative multiuser decoding based on probabilistic data association , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[4]  Lars K. Rasmussen,et al.  Belief Propagation for Coded Multiuser Detection , 2006, 2006 IEEE International Symposium on Information Theory.

[5]  Lars K. Rasmussen,et al.  Iterative Multiuser Decoding with Maximal Ratio Combining , 2004 .

[6]  William T. Freeman,et al.  Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.

[7]  Stephan ten Brink,et al.  Convergence behavior of iteratively decoded parallel concatenated codes , 2001, IEEE Trans. Commun..

[8]  Paul D. Alexander,et al.  Iterative detection in code-division multiple-access with error control coding , 1998, Eur. Trans. Telecommun..

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  J. Hagenauer.  Forward error correcting for CDMA systems , 1996, Proceedings of ISSSTA'95 International Symposium on Spread Spectrum Techniques and Applications.

[11]  Stephen G. Wilson,et al.  Multiuser ML sequence estimator for convolutionally coded asynchronous DS-CDMA systems , 1996, IEEE Trans. Commun..

[12]  Rüdiger L. Urbanke,et al.  The capacity of low-density parity-check codes under message-passing decoding , 2001, IEEE Trans. Inf. Theory.

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

[14]  H. Vincent Poor,et al.  Iterative (turbo) soft interference cancellation and decoding for coded CDMA , 1999, IEEE Trans. Commun..

[15]  Branka Vucetic,et al.  Space-time iterative and multistage receiver structures for CDMA mobile communication systems , 2001, IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202).

[16]  Paul D. Alexander,et al.  An extrinsic Kalman filter for iterative multiuser decoding , 2004, IEEE Transactions on Information Theory.

[17]  Sergio Verdú,et al.  Minimum probability of error for asynchronous Gaussian multiple-access channels , 1986, IEEE Trans. Inf. Theory.

[18]  Lars K. Rasmussen,et al.  Asymptotically optimal nonlinear MMSE multiuser detection based on multivariate Gaussian approximation , 2005, IEEE Transactions on Communications.

[19]  R. Milner,et al.  Weighted extrinsic feedback in the iterative multiuser decoding of coded CDMA , 2006, 2006 Australian Communications Theory Workshop.

[20]  L.K. Rasmussen,et al.  Truncated maximal ratio combining for iterative multiuser decoding , 2005, 2005 Australian Communications Theory Workshop.