Resource Allocation For NOMA-Based Wireless Communication System

The great development of the communication technology and the growing user demands for communication have led to the increasingly lack of radio resources.Therefore,it is an urgent issue to improve the performance of wireless communication systems.On the one hand,spectrum resource is limited,and the low frequency can no longer meet the demands of users for high information transmission rates.Hence,it is urgent to improve the spectrum efficiency(SE)of communication systems.On the other hand,the explosive growth of mobile devices and the rapid rise of data traffic has caused huge energy consumption.The enormous environmental pollution will become increasingly serious,which is not in line with the concept of green and low-carbon development.Thus,it is a crucial issue to deploy the next generation wireless communication network architecture.Besides,how to improve the energy efficiency(EE)of communication systems has become a key problem to be solved.Non-orthogonal multiple access(NOMA),as a significant technique in 5G and beyond wireless communication networks,has attracted widespread attention.Compared to the traditional orthogonal multiple access(OMA),NOMA is capable of expanding connectivity of systems and improving SE,resulting from serving multiple users in the same time-frequency resource block.This thesis focuses on NOMA technology and its application for intensive study.Besides,with the combination of simultaneous wireless information and power transfer(SWIPT),cloud radio access network(C-RANs),massive multiple-input multiple-output(Massive MIMO)and hybrid precoding technology,the resource allocation for NOMA-based wireless communication system is investigated in this thesis.The main contents of this article are as follows:(1)The NOMA-based simultaneous wireless information and power transfer system is studied to maximize the information transmission rate and energy collection simultaneously.Firstly,the model of NOMA-based simultaneous wireless information and power transfer system is constructed.Considering the constraints of the maximum transmit power of the base station,the minimum harvested energy and information transmission rate of each user,the resource allocation optimization problem was proposed to maximize the system information transmission rate and energy collection simultaneously.Since the optimization objectives are conflict with each other and their dimensional are inconsistent,we transform the harvested power into data rate equivalently using Shannon formula,and then the weighted sum method is used to establish a single-objective optimization(SOO)problem.The proposed optimization problem is non-convex,and a deep learning-based resource allocation algorithm is proposed.Numerical results verify the effectiveness of the proposed algorithm.It also illustrates that significant performance gain can be achieved by using NOMA-based simultaneous wireless information and power transfer system scheme compared with the traditional OMA scheme.(2)The resource allocation problem is studied to maximize the energy efficiency of the NOMA-based cloud radio access network.Based on the advantages of cloud radio access network in terms of low cost,low power consumption and better system performance,NOMAbased cloud radio access network is constructed combined with massive MIMO and hybrid precoding technology.Considering the constraints of the forward link capacity and maximum transmit power of the radio remote head,a joint resource allocation problem for energy efficiency maximization was studied.To tackle the energy efficiency maximization problem which is non-convex,a hybrid precoding design and power allocation algorithm is proposed to jointly optimize analog precoding,digital precoding and power allocation.The simulation results verify the convergence and effectiveness of the proposed algorithm.Besides,the simulation results also indicate that the proposed NOMA-based joint optimization algorithm can effectively improve the energy efficiency compared with the traditional OMA-based joint optimization scheme or the two-stage hybrid precoding algorithm.