面對4G邁入5G的時代，為傳統的資源分配帶來了新的挑戰，產生的新問題的主要原因是5G無線系統將需要實現大量物聯網設備(IoT)與人機通信(HTC)的共存，面對大量IoT設備，由於流量特性的不同，對傳統LTE網絡的隨機存取過程和資源分配存在一定的挑戰，其中將造成現存的核心網路產生負載過大的問題，有許多IoT設備同時向基地台發送接入請求，將導致嚴重的訪問衝突，在現有的調度算法中並不能有效地消化嚴重負載的信令接入請求。因此，需要新穎的解決方案來做有效地排解與消耗。
本論文設計將大量IoT裝置依據其地理位置結合成群集，在集群成員當中，我們將選擇一個終端作為聚合器(Aggregator)，負責當作群集的窗口，幫助底下的裝置傳輸大量的資料，以此降低信令的產生，群組內的裝置分為週期性裝置以及非週期性裝置，依據其各自所產生的週期性資料和突發性資料來做傳送的區別，本篇論文依據LTE-A系統下的週期性polling以及隨機存取程序做為基地台和裝置之間的傳輸連線，藉由兩種方式的組合，將可以降低資料的延遲時間，在非週期性資料傳輸中，進一步提出P機率值的變數，藉此計算讓系統維持在最佳狀態，提升整體系統效能。
由模擬結果可知，本論文所提出之方法可以降低物聯網設備的延遲時間，此外，加入P機率值的影響，本論文之方法更能有效提升系統吞吐量。因此，在物聯設備數量眾多的情境下，透過本論文的方法減少大量的爆炸信令，也能維持一定的傳輸品質。

For the 4G into 5G, the traditional resource allocation is facing a new challenge. The reason is that 5G wireless systems will need to achieve the coexistence of the mass Internet of Thing (IoT) network and the Human Type Communication (HTC). Due to different traffic flow, IoT device has challenges to the random access process and resource allocation algorithm of traditional LTE networks. Which will cause the existing core network overload. There are many IoT devices sending access requests to the base station at the same time. It will cause serious access collision. The existing scheduling algorithm cannot effectively digest the heavy signaling. Therefore, it need a innovative solution to solve this problem.
In this paper, we group the massive IoT devices into clusters based on their geographical location. We choose a device in the cluster as Aggregator. It is a bridge for other devices in the cluster to transmit the large data, and reduce the signaling. The devices are be divided into periodic devices and non-periodic devices, according to the way they produce the data. The periodic polling of the LTE-A system and the random access procedure are be used as the transmission link between the base station and the Aggregator. The combination of the two methods will reduce the latency of the data. Next, we propose a variable probability of P to maintain the system in the best state and improve the system performance.
The method proposed in this paper can reduce the delay time of the IoT devices and improve the system throughput by the probability P. Therefore, we can use this method to reduce the large signaling, also to maintain a transmission quality.

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