# On the Efficient Simulation of the Distribution of the Sum of Gamma-Gamma Variates with Application to the Outage Probability Evaluation Over Fading Channels

- Handle URI:
- http://hdl.handle.net/10754/612739
- Title:
- On the Efficient Simulation of the Distribution of the Sum of Gamma-Gamma Variates with Application to the Outage Probability Evaluation Over Fading Channels
- Authors:
- Abstract:
- The Gamma-Gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverbation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels. In this respect, assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance. In many circumstances, this is intimately related to the difficult question of analyzing the statistics of a sum of Gamma-Gamma random variables. Answering this question is not a simple matter. This is essentially because outage probabilities encountered in practice are often very small, and hence the use of classical Monte Carlo methods is not a reasonable choice. This lies behind the main motivation of the present work. In particular, this paper proposes a new approach to estimate the left tail of the sum of Gamma-Gamma variates. More specifically, we propose a mean-shift importance sampling scheme that efficiently evaluates the outage probability of L-branch maximum ratio combining diversity receivers over Gamma-Gamma fading channels. The proposed estimator satisfies the well-known bounded relative error criterion, a well-desired property characterizing the robustness of importance sampling schemes, for both identically and non-identically independent distributed cases. We show the accuracy and the efficiency of our approach compared to naive Monte Carlo via some selected numerical simulations.
- KAUST Department:
- Issue Date:
- Jun-2016
- Type:
- Technical Report

- Appears in Collections:
- Technical Reports

# Full metadata record

DC Field | Value | Language |
---|---|---|

dc.contributor.author | Ben Issaid, Chaouki | en |

dc.contributor.author | Ben Rached, Nadhir | en |

dc.contributor.author | Kammoun, Abla | en |

dc.contributor.author | Alouini, Mohamed-Slim | en |

dc.contributor.author | Tempone, Raul | en |

dc.date.accessioned | 2016-06-13T12:10:33Z | - |

dc.date.available | 2016-06-13T12:10:33Z | - |

dc.date.issued | 2016-06 | - |

dc.identifier.uri | http://hdl.handle.net/10754/612739 | - |

dc.description.abstract | The Gamma-Gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverbation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels. In this respect, assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance. In many circumstances, this is intimately related to the difficult question of analyzing the statistics of a sum of Gamma-Gamma random variables. Answering this question is not a simple matter. This is essentially because outage probabilities encountered in practice are often very small, and hence the use of classical Monte Carlo methods is not a reasonable choice. This lies behind the main motivation of the present work. In particular, this paper proposes a new approach to estimate the left tail of the sum of Gamma-Gamma variates. More specifically, we propose a mean-shift importance sampling scheme that efficiently evaluates the outage probability of L-branch maximum ratio combining diversity receivers over Gamma-Gamma fading channels. The proposed estimator satisfies the well-known bounded relative error criterion, a well-desired property characterizing the robustness of importance sampling schemes, for both identically and non-identically independent distributed cases. We show the accuracy and the efficiency of our approach compared to naive Monte Carlo via some selected numerical simulations. | en |

dc.subject | Gamma-Gamma | en |

dc.subject | Monte Carlo | en |

dc.subject | Generalized-K | en |

dc.subject | Importance Sampling | en |

dc.subject | bounded relative error | en |

dc.subject | Outage Probability | en |

dc.subject | maximum ratio combining (MRC) | en |

dc.title | en | |

dc.type | Technical Report | en |

dc.contributor.department | Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division | en |

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