## Optimum multi-user signal detection |

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### Contents

INTRODUCTION | 1 |

ANALYSIS OF ERROR PROBABILITY OF OPTIMUM MULTIUSER DETECTORS | 28 |

ASYMPTOTIC EFFICIENCY AND COMPLEXITY OF OPTIMUM MULTIUSER DETECfORS | 73 |

Copyright | |

3 other sections not shown

### Common terms and phrases

active users additive decomposition backward binary bounding series Chapter complexity computation conventional single-user convergence crosscorrelations decision algorithm decomposable demodulation derive detection problem dynamic programming dynamic programming algorithm equal equation error sequences exists finite finite-horizon follows Gaussian noise Hence ID detector IEEE Trans interfering users intersymbol interference k'h user KNAPSACK likelihood function likelihood ratio likelihood sequence detector linear locally optimum lower bound Markov process matched filter maximizes maximum likelihood sequence minimum error probability modulated multi-user asymptotic efficiencies multi-user signal multiple-access node nonzero NP-hard number of users obtain operators optimality optimum K ID optimum multi-user detectors optimum sequence point process Poisson probability of error Proof Proposition 2.1 Proposition 22 recursion relative delays result satisfied Section shown signal constellation signal-to-noise ratio Spread-Spectrum subset sufficient condition sufficient statistics tion transmitted sequence transmitted symbols upper bound users of interest Viterbi algorithm waveforms white Gaussian noise zero