A novel random access technique for massive machine-type communications with heterogeneous users

• Main Authors: CHENG, HAO-PING, 鄭皓平
• Other Authors: HUANG, YU-CHIH
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/3cfgyt

碩士 === 國立臺北大學 === 通訊工程研究所 === 107 === As 5G technology developing further, the requirements of different applications become clear. There are three types of use cases. Enhanced mobile broadband(eMBB),ultra-reliable and low latency communications(URLLC) and massive machine-type communications(mMTC). First is eMBB, which is used for wider bandwidth applications like mobile broadband service. And URLLC applications require a more reliable and low latency connection. Like factory automation and remote surgery. The last one, mMTC, is the focus of this thesis. mMTC is used in a scenario that has a large amount of devices and few data transmission. In this type of application, it is waste to establish a connection before every transmission. Sometimes resource that used for connection are more than for transmission. So, uncoordinated multiple access(UMA) is applied. Traditional UMA like traditional slotted ALOHA can transmit data without establishing a connection. But it can only achieve 37% of efficiency the most. In G. Liva, “Graph-based analysis and optimization of contention resolution diversity slotted ALOHA”, Liva proposed an IRSA technique base on slotted ALOHA, which using probability distribution that is well designed to decide number of retransmission times. Also, at receiver, applying successive interference cancellation(SIC) to deal with packets collision issue. IRSA technique was proofed that can achieve 100% efficiency if choosing a well-designed probability distribution. In this thesis, we try to apply IRSA on mMTC scenarios, which usually contain many types of heterogeneous users in a transmission time. And we also introduce the concept of non-orthogonal multiple access(NOMA) into IRSA to realize intra-slot SIC. This could greatly improve the efficiency of the system. To analyze efficiency of IRSA with NOMA, we proposed a new type of Tanner graph to represent for IRSA with NOMA. Also, a novel density evolution that is multi-dimensional instead. Then we can use linear programming to optimize the probability distribution. Final, we did a series of simulations to verify our analysis. Results confirm that IRSA with NOMA can easily achieve 100% efficiency in mMTC that contain heterogeneous users.