Asymptotically optimal nonlinear MMSE multiuser detection based on multivariate Gaussian approximation
Journal article, 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, Computer Science and Engineering (Chalmers)

Lars K. Rasmussen

University of South Australia

IEEE Transactions on Communications

0090-6778 (ISSN)

Vol. 54 8 1427-1438

Subject Categories

Computer and Information Science



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