隨著無線技術的發展，物聯網（Internet of thing; IoT）帶給人們更方便、更人性化的服務。形成物聯網的關鍵技術是機器類型通訊（machine-type communication; MTC），支援此技術的通訊設備擁有以下幾種特點；低功耗、低成本、低資料量傳輸、低移動性或不移動、機器與機器之間可以自主化進行資料傳輸而不需要人為控制，正是這些特點構成物聯網。
為了使物聯網的應用得以實現，第三代合作夥伴（3rd generation partnership project）提出長期演進技術之機器類型通訊（long term evolution-machine-type communication; LTE-M）的新標準，定義機器對機器（machine-to-machine; M2M）通訊應用的規範。在未來物聯網的環境中，一個基地台的覆蓋範圍內佈署著大量的MTC裝置（MTC device; MTCD）以及用戶裝置（user equipment; UE），這些裝置透過相同的隨機存取程序（random access procedure; RAP）向基地台請求通道資源。然而用於隨機存取（random access; RA）的實體隨機存取通道（physical random access channel; PRACH）的資源是有限的，當大量的裝置在短時間內向基地台請求通道資源會造成通道壅塞，隨機存取的成功率下降且等待與基地台建立連線的時間變長。
本論文針對通道壅塞的問題提出解決方案，透過分類分群的機制來降低同時發起隨機存取的裝置數量。首先會將所有的裝置進行分群，並且每一個群組會有一個協調者，協調者負責指派隨機存取機會（random access opportunity; RAO）給組員，使一次隨機存取機會中成功隨機存取的裝置數量最佳化，藉此解決PRACH壅塞的問題。模擬結果顯示，在大型智慧城市的環境下，當有大量裝置向基地台請求通道資源時，本論文提出的方法可以在更短的時間內解決通道壅塞。

The concept of Internet of thing (IoT) implements in real life through machine-type communication (MTC). The features of MTC device (MTCD) are low power consumption, low complexity, small data transmission, rarely moving or motionless, machine-to-machine (M2M) communication without human intervention and so on, that are the key points in IoT.
Third-generation partnership project (3GPP) has defined the specification of long-term evolution-MTC (LTE-M) to satisfy the demand of M2M applications.
There is a massive number of MTCDs for IoT applications in the future, when a massive number of MTCDs send the connection request to the base station (BS) via random access procedure (RAP), physical random access channel (PRACH) will congest due to the resources of PRACH are limited, it causes the longer delay for access the network, even the services interrupted.
This thesis focuses on solving PRACH congestion problem by grouping and classifying, the proposed mechanism separates MTCDs into several groups and the head of each group assigns random access opportunity (RAO) to its members. In this way controls the number of MTCDs initiating RA simultaneous, and solves PRACH congestion problem.
The simulation results in different scenarios show that the proposed mechanism effectively solves PRACH congestion problem with shorter time or higher success rate.

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