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Title: | Data Backlog Analysis in Energy Harvesting Communication Systems |

Authors: | Riheng Jia;Jinbei Zhang;Peng Liu;Xiao-Yang Liu;Xiaoying Gan;Xinbing Wang |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Energy harvesting enables the wireless devices to obtain energy for communication from the ambient environment. A general theme in prior works is to investigate the power scheduling policies to increase the utility ratio of the harvested energy, which arrives at random. One key assumption is the infinite data backlog, which means that as long as there is energy, there is data to transmit. However, in real systems, the buffer size is limited, and the arrival of data is also random. When the data backlog fills up the buffer, the subsequent arrival packets will be discarded directly. Therefore, we are motivated to jointly consider the data arrival and energy arrival processes in an energy harvesting communication system (EHCS). Specifically, we first derive the maximum average throughput <inline-formula> <tex-math notation="LaTeX">\bar {r} </tex-math></inline-formula> that EHCS can support with a simple online power scheduling scheme. Then, given a data arrival process whose average rate <inline-formula> <tex-math notation="LaTeX">\lambda <\bar {r} </tex-math></inline-formula>, we characterize the average data backlog for both constant and random data arrivals. Some further analyses are conducted to the variation of data backlog. To achieve a same packet drop rate, the buffer size needed for constant data arrivals is much smaller than that for random data arrivals, which can be seen from both our theoretical and simulation results. The analysis in this paper initiates a first step towards a more dynamic energy harvesting system, where data arrivals are of importance. |

Description: | |

URI: | http://localhost/handle/Hannan/150439 |

volume: | 5 |

More Information: | 5702, 5712 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7879260.pdf | 13.52 MB | Adobe PDF |

Title: | Data Backlog Analysis in Energy Harvesting Communication Systems |

Authors: | Riheng Jia;Jinbei Zhang;Peng Liu;Xiao-Yang Liu;Xiaoying Gan;Xinbing Wang |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Energy harvesting enables the wireless devices to obtain energy for communication from the ambient environment. A general theme in prior works is to investigate the power scheduling policies to increase the utility ratio of the harvested energy, which arrives at random. One key assumption is the infinite data backlog, which means that as long as there is energy, there is data to transmit. However, in real systems, the buffer size is limited, and the arrival of data is also random. When the data backlog fills up the buffer, the subsequent arrival packets will be discarded directly. Therefore, we are motivated to jointly consider the data arrival and energy arrival processes in an energy harvesting communication system (EHCS). Specifically, we first derive the maximum average throughput <inline-formula> <tex-math notation="LaTeX">\bar {r} </tex-math></inline-formula> that EHCS can support with a simple online power scheduling scheme. Then, given a data arrival process whose average rate <inline-formula> <tex-math notation="LaTeX">\lambda <\bar {r} </tex-math></inline-formula>, we characterize the average data backlog for both constant and random data arrivals. Some further analyses are conducted to the variation of data backlog. To achieve a same packet drop rate, the buffer size needed for constant data arrivals is much smaller than that for random data arrivals, which can be seen from both our theoretical and simulation results. The analysis in this paper initiates a first step towards a more dynamic energy harvesting system, where data arrivals are of importance. |

Description: | |

URI: | http://localhost/handle/Hannan/150439 |

volume: | 5 |

More Information: | 5702, 5712 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7879260.pdf | 13.52 MB | Adobe PDF |

Title: | Data Backlog Analysis in Energy Harvesting Communication Systems |

Authors: | Riheng Jia;Jinbei Zhang;Peng Liu;Xiao-Yang Liu;Xiaoying Gan;Xinbing Wang |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Energy harvesting enables the wireless devices to obtain energy for communication from the ambient environment. A general theme in prior works is to investigate the power scheduling policies to increase the utility ratio of the harvested energy, which arrives at random. One key assumption is the infinite data backlog, which means that as long as there is energy, there is data to transmit. However, in real systems, the buffer size is limited, and the arrival of data is also random. When the data backlog fills up the buffer, the subsequent arrival packets will be discarded directly. Therefore, we are motivated to jointly consider the data arrival and energy arrival processes in an energy harvesting communication system (EHCS). Specifically, we first derive the maximum average throughput <inline-formula> <tex-math notation="LaTeX">\bar {r} </tex-math></inline-formula> that EHCS can support with a simple online power scheduling scheme. Then, given a data arrival process whose average rate <inline-formula> <tex-math notation="LaTeX">\lambda <\bar {r} </tex-math></inline-formula>, we characterize the average data backlog for both constant and random data arrivals. Some further analyses are conducted to the variation of data backlog. To achieve a same packet drop rate, the buffer size needed for constant data arrivals is much smaller than that for random data arrivals, which can be seen from both our theoretical and simulation results. The analysis in this paper initiates a first step towards a more dynamic energy harvesting system, where data arrivals are of importance. |

Description: | |

URI: | http://localhost/handle/Hannan/150439 |

volume: | 5 |

More Information: | 5702, 5712 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7879260.pdf | 13.52 MB | Adobe PDF |