Research On Joint Resource Allocation Scheme Of NOMA System

With the wide application of 5G technology,the amount of user access in mobile communication networks has increased significantly,and users have put forward higher requirements on communication quality and communication speed.Traditional technologies have been unable to meet the communication needs,and NOMA technology can improve the capacity of multi-user systems and improve resource utilization efficiency.As one of the 5G core candidate technologies NOMA has received extensive attention.Based on the consideration of improving the capacity of the communication system and saving power,this thesis studies the power and subcarrier allocation of NOMA technology and its joint allocation.The main work of this thesis is as follows:Firstly,several power allocation algorithms commonly used in the NOMA system are analyzed.The existing NOMA system power allocation algorithm has some defects in global search ability and search complexity.The genetic algorithm is combined with NOMA power allocation.The NOMA power allocation algorithm based on genetic algorithm,which distributes power to users based on the criterion of maximizing the total transmission rate,has a faster and more random search capability and a relatively lower search complexity.It can be seen that the algorithm not only can abate the computational complexity of the system but also ensure system performance effectively.Secondly,based on the purpose of reducing the system transmit power and combining with the existing system model,an algorithm based on the deleted NOMA system for joint optimization of power allocation and subcarrier allocation is proposed.An optimal solution for user power allocation with fixed subcarriers is studied.Based on this scheme,a low complexity algorithm for joint optimization of subcarrier allocation and power allocation is proposed.The simulation results show that compared with the traditional OFDM-based frequency division multiple access and static NOMA resource allocation scheme,the performance of the algorithm is close to optimal and the power consumption of the system is significantly reduced.