裝置對裝置通訊(Device-to-Device communications)允許D2D pairs重複使
用CUE(cellular user equipment)之頻譜資源，鄰近的裝置不須透過基地台(Base
Station, BS)即可直接通訊，能提高的頻譜使用效率、服務裝置數量，但重複使
用相同資源的裝置間會相互干擾，所以藉由無線通訊資源分配以及傳輸功率控
制，避免反而降低頻譜使用效率。
D2D常應用在行動裝置和的通訊，這類裝置多是由電池提供電源，但電池容
量有限，若電量消耗太快，則裝置運行時間會變短。因此，這篇論文將探討在
滿足所有裝置的最小速率要求之下，使得系統能源效率最大化的問題。
能源效率問題是一個分式非線性問題，沒有辦法直接得到最佳解，我們提
出一個迭代式的功率控制演算法，利用Dinkelbach's method，並且在每一次迭
代中把問題連續近似成幾何規劃(geometric programming, GP)問題。因為前述
的方法花費較多時間，我們嘗試使用機器學習(Machine Learning)來協助解決問
題， 並且改寫一個現存的能源效率最佳化演算法，來快速產生大量的訓練資料
集(Training Data Set)。
根據實驗的結果，我們的方法不僅在主要目標能源效率上表現良好，整體的
系統速率總和也有很大的改進。我們也可以看到加入機器學習之後， 對於運算
時間和效果的影響。

D2D (Device-to-Device) communications allow D2D pairs to reuse cellular user
equipment's (CUE) radio resources, and to communicate directly without routing
through the base station (BS). The UEs that reuses the same radio resources will
cause mutual interference, so we need better radio resources allocations and transmit
power control. Additionally, D2D communications usually apply to mobile
devices, if the power consumption is too high, the lifetime of devices and system
will be short.
In this thesis, we propose an iterative algorithm that utilizes optimization
techniques to maximize energy efficiency (EE). The EE maximization problem
is a fractional nonlinear programming problem and cannot get the optimal solution
directly, so we use the Dinkelbach's method, and successively approximate the
problem to a geometric programming (GP) problem. However, the aforementioned
optimization algorithm is time-consuming, so we try to use machine learning to
help solve the problem quickly. We modify an existing EE optimization algorithm
to generate large training data set rapidly. Simulation results show that our
method not only improves the system EE, but also reaches high system sum rate.
We also see the impact of machine learning on running time and performance.

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