### Abstract

This paper studies randomly spread code-division multiple access (CDMA) and multiuser detection in the large-system limit using the replica method developed in statistical physics. Arbitrary input distributions and flat fading are considered. A generic multiuser detector in the form of the posterior mean estimator is applied before single-user decoding. The generic detector can be particularized to the matched filter, decorrelator, linear minimum mean-square error (MMSE) detector, the jointly or the individually optimal detector, and others. It is found that the detection output for each user, although in general asymptotically non-Gaussian conditioned on the transmitted symbol, converges as the number of users go to infinity to a deterministic function of a "hidden" Gaussian statistic independent of the interferers. Thus, the multiuser channel can be decoupled: Each user experiences an equivalent single-user Gaussian channel, whose signal-to-noise ratio (SNR) suffers a degradation due to the multiple-access interference (MAI). The uncoded error performance (e.g., symbol error rate) and the mutual information can then be fully characterized using the degradation factor, also known as the multiuser efficiency, which can be obtained by solving a pair of coupled fixed-point equations identified in this paper. Based on a general linear vector channel model, the results are also applicable to multiple-input multiple-output (MIMO) channels such as in multiantenna systems.

Original language | English (US) |
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Pages (from-to) | 1983-2010 |

Number of pages | 28 |

Journal | IEEE Transactions on Information Theory |

Volume | 51 |

Issue number | 6 |

DOIs | |

State | Published - Jun 1 2005 |

### All Science Journal Classification (ASJC) codes

- Information Systems
- Computer Science Applications
- Library and Information Sciences

### Keywords

- Channel capacity
- Code-division multiple access (CDMA)
- Free energy
- Multiple-input multiple-output (MIMO) channel
- Multiuser detection
- Multiuser efficiency
- Replica method
- Statistical mechanics

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## Cite this

*IEEE Transactions on Information Theory*,

*51*(6), 1983-2010. https://doi.org/10.1109/TIT.2005.847700