隨著聯網需求的提升與普及化，高效能且高穩定的網路服務日漸重要，隨者聯網設備的日益增長，無線電頻率將面臨不敷使用的狀況。超密集可見光通訊網路作為一個可能的解決方案，其具有更加寬廣的頻譜與高效能的優點。在可見光網路中，非對成剪裁光正交分頻多工是常用的調變多工，藉由主動增加子載波的改良，層化非對成剪裁光正交分頻多工可以增加可見光通訊的傳輸速率。在層化非對成剪裁光正交分頻多工的超密集可見光通訊網路中，非正交多重存取與正交多工存取技術的混合使用可以提升效能並減少小區間干擾。
層化非對成剪裁光正交分頻多工的超密集可見光通訊網路中的資源配置問題需要考慮有限的資源與調變模式選擇的靈活性，並在兩者的取捨中最大化總流量。過往有文獻將其轉換成動態規劃的問題，但求得最佳解答的代價是必須經歷耗時的計算過程。也有文獻只透過逐層選擇調變模式，此方法可以快速完成配置，但也會使較多的用戶無法被服務。為了保持與前一種方法相同的總流量，此方法也需要耗費較多的功耗。本論文分析不同的調變模式配置可以達到的傳輸速率與相對應所需的功耗。然後提出一種具有成本效益的資源配置，在給定的傳輸速率需求之下，給予用戶一個使用盡可能低的功耗，並且能滿足速率需求的調變模式配置。本文也對於剩餘資源提出一種剩餘資源配置，希望能讓所有已被服務的用戶都能得到剩餘資源，提升總體效能。最後，模擬數據顯示我們提出的剩餘資源配置在總體流量和功率效率上分別有大約 40% 和 50% 的提升。

High-efficiency and high-stability network services are becoming increasingly important with improving and popularizing networking requirements. With the increasing number of internet devices, radio frequencies (RF) will be insufficient. As a possible solution, the ultra-dense visible light communication (UD-VLC) network has the advantages of a wider spectrum and high performance. By using more subcarriers, layered asymmetrically clipped optical OFDM (LACO-OFDM) achieves higher rate than ACO-OFDM, which is commonly used in VLC. For LACO-OFDM UD-VLC multiuser downlink network, the hybrid non-orthogonal multiple access (NOMA) and OMA can enhance the performance and against the inter-cell interference (ICI).
Resource allocation in LACO-OFDM UD-VLC networks needs to consider both resource constraints and flexibility in modulation mode selection. A previous work calculated all assignments and got the most perfect solution, which took a lot of computing time. Another work simply assigned the modulation modes layer by layer. The cost of reducing computation time and maintaining throughout level is the more power consumption and higher outage probability. In this paper, we analyze the power consumption and achievable rate of different modulation mode schemes. We then propose a cost-effective resource allocation, which assigns user equipment (UE) a modulation mode scheme that consumes the lowest power while satisfying the rate requirement. For the remaining resources, we propose a leftover resource allocation that benefits all served UEs and thus improves the performance, with 40% increase in throughput and 50% increase in power efficiency.

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