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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68677

    Title: 基於Q-Learning之雙向能量採集通訊傳輸方法設計與模擬;Design and Simulation of Q-Learning Based Transmission Schemes for Two-Way Energy Harvesting Communications
    Authors: 吉米;Julien,Jean-jimmy
    Contributors: 通訊工程學系
    Keywords: 雙向通訊;綠能採集;馬可夫決策過程;通道;無線感知網路
    Date: 2015-07-31
    Issue Date: 2015-09-23 14:00:49 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在本文中,雙向通信能量採集系統,研究了利用馬可夫決策過程(MDP)。這個過程提供了在各種情況下的結果是部分地決策者和部分地隨機的控制下,及模擬決策的數學框架。通信傳達從一個區域或指向另一個通過電磁,聲學和許多其他波的物理信道的信息。這裡的信息通常是表現為電流或電壓,它們可以是連續的,並具有一組已知的可能值的可能值,也離散變量的無限數量。該通信系統連接的機器,包括網絡系統傳達數據雙向包括多個其他節點,也是存儲和召回信息的存儲系統。
    ;In this thesis, a two-way communication energy harvesting system is studied by the use of Markov Decision Process (MDP). This process is the ultimate process that provides a mathematical framework for modeling decision-making in various situations where the outcomes are partly under the control of the decision maker and partly random. Communication conveys the information from one area or points to another through physical channels that propagate particle density, electromagnetic, acoustic and many other waves. The information referred here is usually manifest as currents or voltages, and they may be continuous and have an infinite number of possible values and also discrete variables having a set of known possible values. This communication system links machines which include networks systems which convey data two way including multiple other nodes and also the memory systems that store and recall information.
    Both the data and the energy arrivals at the transmitter are modeled as Markov processes. The delay-limited communication is always considered by taking the assumption that the underlying channel is a blocking fading with memory and also the instantaneous channel state information is again available in both the receiver and the transmitter. The total transmitted data which is expected during the transmitter’s activation period is maximized under different sets of assumptions; these are three sets regarding the available information in the transmitter concerning the underlying stochastic processes.
    Therefore energy harvesting (EH) has actually emerged as a promising technology that expands the communication industry and networks. For instance machine to machine or wireless sensor network which complements the current battery-powered transceivers by collecting and harvesting the ambient available energy including the solar, thermal- gradient, and vibration. Unlike the battery limited services, the energy harvesting system by employing the Markov Decision process can theoretically operate over an unlimited time horizon. Therefore to optimize the communication performance and with the sporadic arrival energy in limited amounts, it is advisable to maximize the transmission policy by using the available information about the energy and data arrival processes.
    Appears in Collections:[通訊工程研究所] 博碩士論文

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