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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81014


    Title: 基於長短期記憶神經網路之綠能無線通訊系統功率控制研究;Long Short-Term Memory Network-based Power Control for Green Wireless Communications
    Authors: 萬家妤;Wan, Chia-Yu
    Contributors: 通訊工程學系
    Keywords: 能量獵取;最佳化功率控制;深度學習;長短期記憶神經網路;energy harvesting;optimal power control;deep learning;long short-term memory
    Date: 2019-08-21
    Issue Date: 2019-09-03 15:25:20 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著物聯網的日益普及,由於無線通訊裝置上的電池儲存空間有限,其能源消耗成為一個具有挑戰性的問題。由於能源獵取技術的出現,通過從太陽能等環境中獵取和儲存可充式電池的能量,可以有效地解決這問題。傳統的方法是規劃在未來一段時間內的能量使用的情況,稱為能量獵取通訊的功率控制,也就是採用所謂的凸優化(Convex Optimization)技術。該方案需要已知未來能量獵取和通道增益資訊,難以在實際應用中實現。因此在本文中,吾人設計一種基於深度學習的單用戶和多用戶能量獵取通訊框架,而該框架只需已知當前能量獵取和通道增益的資訊,便可預測未來功率控制值的方法。以單用戶例子來說,吾人利用以前太陽能數據通過凸優化計算得到最佳解,然後將其輸入長短期記憶網路(Long Short-Term Memory)訓練,來預測未來功率控制值。延伸單用戶系統,吾人利用加權和最小均方差(WMMSE)演算法得到的功率控制參考解,研究一種基於長短期記憶網路多用戶功率控制方案。最後通過電腦模擬驗證了所提出的基於長短期記憶網路的功率控制方案的有效性。實驗結果表明,與貪婪功率控制方案相比,吾人所提出基於長短期記憶網路的功率控制方案能夠獲得更好的系統吞吐量。;With the increasing prevalence of Internet of things, the energy consumption problem in becomes a challenging issue due to the limited battery storage on wireless communication devices. Thanks to the emergence of energy harvesting techniques, this problem can be efficiently solved by scavenging and storing energy in rechargeable batteries from ambient environments like solar. A conventional approach to schedule the energy usage over the future of a period of time, which is also called control power in energy harvesting communications, is to apply the convex optimization technique. This scheme relies on the perfect knowledge of the future information of energy harvesting conditions and channel gains control, which makes it difficult to be implemented in real applications. In this thesis, deep learning design frameworks, which only require the past knowledge of energy harvesting and channel conditions, are proposed to predict the future power control values in single-user and multi-user energy harvesting communications. For the single-user case, we utilize the historical solar data to calculate the optimal solutions via convex optimization, which is then used in the training of a long short-term memory network (LSTM) for predicting power control values. As an extension, an LSTM-based power control scheme is investigated in the multi-user scenario by using the reference power control solutions obtained from a weighted sum minimum mean-square-error (WMMSE) algorithm. The effectiveness of the proposed LSTM-based power control schemes is finally evaluated by computer simulations, and the results show that the proposed schemes can achieve sufficient good system throughput as compared with the baseline solutions.
    Appears in Collections:[通訊工程研究所] 博碩士論文

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