## Abstract

We explore the performance of minimum mean-square error (MMSE) multiuser receivers in wireless systems where the signatures are modeled as random and take values in complex space. First we study the conditional distribution of the output multiple-access interference (MAI) of the MMSE receiver. By appealing to the notion of conditional weak convergence, we find that the conditional distribution of the output MAI, given the received signatures and received powers, converges in probability to a proper complex Gaussian distribution that does not depend on the signatures. This result indicates that, in a large system, the output interference of the MMSE receiver is approximately Gaussian with high probability, and that systems with MMSE receivers are robust to the randomness of the signatures. Building on the Gaussianity of the output interference, we then take the quality of service (QoS) requirements as meeting the signal-to-interference ratio (SIR) constraints and identify the network capacity of single-class systems with random spreading. The network capacity is expressed uniquely in terms of the SIR requirements and received power distributions. Compared to the network capacity corresponding to the optimal signature allocation, we conclude that at the cost of transmission power, the gap between the network capacity corresponding to optimal signatures and that corresponding to random signatures can be made arbitrarily small. Therefore, from the viewpoint of network capacity, systems with MMSE receivers are robust to the randomness of signatures.

Original language | English (US) |
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Pages (from-to) | 2114-2122 |

Number of pages | 9 |

Journal | IEEE Transactions on Information Theory |

Volume | 48 |

Issue number | 7 |

DOIs | |

State | Published - Jul 2002 |

## Keywords

- Central limit theorem
- Conditional weak convergence
- Martingale difference array
- Minimum mean-square error (MMSE) receiver
- Proper complex random variable
- Random signature

## ASJC Scopus subject areas

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