在Li-Fi微微微蜂巢式網路中，Li-Fi存取點的緊密排列導致嚴重的區間干擾，進而造成效能下降；以使用者為中心之網路其依據使用者位置分佈，動態組成多個不規則細胞，減輕了干擾的狀況。在此篇論文，我們以提升能源效率為目標，分別提出使用者分群演算法，以及調變等級選擇及功率配置演算法。
　　在先前的研究中，分群演算法大部分僅考慮把位置較靠近的使用者一一分群，卻未考慮群與群間的距離，因此我們提出新的分群演算法，將使用者依據距離分群之外，並且將建立好的群之間的距離分得儘量遠；在調變等級選擇及功率配置演算法方面，我們採用多重選擇背包問題解決，除此之外，先前的研究大多將干擾項以最差情況進行估算，在超密度的Li-Fi網路下，這樣的估算會造成效能大幅下降，因此我們提出新的不需迭代的干擾項估算方法，將最差情況的上限壓得更低，如此一來可大幅提升效能。在能源效率方面，在我們所提出的使用者分群演算法可達到最多約87.5%的改善幅度。

In traditional Light-Fidelity (Li-Fi) attocell networks, the dense structure of Li-Fi access points (APs) leads to severe inter-cell interference (ICI). User-centric (UC) networks mitigate the inter-cell interference, which dynamically reform amorphous cells as the user mobility varies in time. In this thesis, we propose a new user-centric clustering and joint modulation order selection algorithm respectively with the objective of maximizing energy efficiency (EE).
In previous studies, clustering algorithms usually only aim at minimizing the distance from the centroid of the cluster to each user within the cell. Since inter-cell interference is the main challenge in Li-Fi networks, separating each cells as far as possible is also important. Therefore, in this thesis, we propose a new clustering algorithm that also considers the distance among each constructed cells. On the other hand, we leverage multi-choice knapsack problem for joint modulation order selection and power allocation. Furthermore, different from previous studies, instead of using worst-case estimated interference, a tighter-upper-bounded estimated interference is proposed, yielding better energy efficiency as the power consumption is further reduced. Using our proposed user-centric clustering algorithm, it can achieve up to approximated 87.5 % improvement in terms of energy efficiency.

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