Asymptotically optimal nonlinear MMSE multiuser detection based on multivariate Gaussian approximation
Artikel i vetenskaplig tidskrift, 2006

In this paper, a class of nonlinear minimum mean-squared error multiuser detectors is derived based on a multivariate Gaussian approximation of the multiple-access interference for large systems. This approach leads to expressions identical to those describing the probabilistic data association (PDA) detector, thus providing an alternative analytical justification for this structure. A simplification to the PDA detector based on approximating the covariance matrix of the multivariate Gaussian distribution is suggested, resulting in a soft interference-cancellation scheme. Corresponding multiuser soft-input, soft-output detectors delivering extrinsic log-likelihood ratios are derived for application in iterative multiuser decoders. Finally, a large-system performance analysis is conducted for the simplified PDA, showing that the bit-error rate (BER) performance of this detector can be accurately predicted and related to the replica method analysis for the optimal detector. Methods from statistical neurodynamics are shown to provide a closely related alternative large-system prediction. Numerical results demonstrate that for large systems, the BER is accurately predicted by the analysis and found to be close to optimal performance.


Peng Hui Tan

Chalmers, Data- och informationsteknik

Lars K. Rasmussen

University of South Australia

IEEE Transactions on Communications

0090-6778 (ISSN)

Vol. 54 8 1427-1438


Data- och informationsvetenskap



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