Key Technologies Research And Performance Evaluation Of NOMA System

As a fact of the rapid development of 5G mobile communication technology, the in-depth integration of Internet and Internet of Things(IOT), and the fast rise of intelligent terminals, existing mobile communication systems and key technologies are facing more challenges.In case of decreasing spectrum resource and large-scale intelligent terminal connection, multiple access scheme transforms from the Orthogonal Multiple Access (OMA) to the Non-orthogonal Multiple Access (NOMA). NOMA, as the candidate scheme for the future 5G multi-access, can enhance the efficiency of the spectrum as well as increase the number of user connections by virtue of its non-orthogonal property. Therefore, the research and performance evaluation for NOMA have a very important significance.Instead of scheduling just one user at one time-frequency resource,NOMA could schedule multiple users by non-orthogonalizing time-frequency resources, which is different from the traditional OMA system. The sender superposes multi-user transmission signals in the power/code domain and the receiver uses Successive Interference Cancellation (SIC) to decode and restore the original signals. In this paper,we research the key technologies of NOMA, including user pairing,power allocation, resource scheduling and MIMO (Multiple-input Multiple-output) fusion. First we proposed an adaptive power allocation algorithm,which compromises the system performance, computational complexity and signaling overhead by adjusting the power allocation factor set. Then we improved the NOMA scheduling algorithm based on Proportional Fair (PF). By using the suitable proportional fair factor coefficients to adjust the proportion of the NOMA and OMA scheduling mode, we approached a better NOMA performance gain. Finally we researched on multi-user superposition transmission candidate schemes and proposed a NOMA joint optimization scheme based on Gray-mapping, which realizes the joint-modulation and Gray-mapping to reduce signaling overhead.We estimated and optimized the performance of NOMA at C++system-level simulation platform. The simulation results show that, for the cell average and edge throughput, the optimized NOMA scheme gains 15.6% and 16.9% compared to the traditional OMA scheme. It also gains 1.2% and 3.5% performance compared to the NOMA joint optimization scheme based on Gray-mapping, which sacrifices some unnecessary performance gain to decrease the system complexity and the signaling overhead.