| Summary: | 碩士 === 國立中央大學 === 通訊工程學系 === 107 === There are three main uses cases for 5th generation mobile networks (5G), one of the cases is Massive Machine Type Communications (mMTC), which is applied to Internet of Things. To implement this scenario, someone has proposed the grant-free transmission and Sparse Code Multiple Access (SCMA), that means users can transmit data over the predefined resource to improve the resource usage.
In uplink grant-free SCMA transmission, UE has to choose CTU to uplink data according to mapping rule. The CTU would collided when more than two UEs choose the same CTU to uplink data. To reduce collision rate and improve transmission efficiency, this thesis proposed Two-stage CTU Allocation method (TCA), which intend to make UEs own dedicated CTU only when the UEs has data to transmit. Hoping to have better results, this thesis also attempts to combine TCA with machine learning for resource allocation.
According to the simulation of this thesis, TCA has good performance in several aspects, and it can have better performance in situation with higher traffic load. However, the improvement of resource allocation by using TCA with machine learning is suitable for scenarios with lower number of UEs and its improvement is limited.
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