Performance Analysis And Optimization Of Uplink Cell-free Massive MIMO-NOMA Systems

Cell-free massive multiple-input multiple-output(MIMO)is a new wireless communication technology that can effectively increase the system’s capacity by using large-scale antenna arrays at both the transmitting and receiving ends.By serving all users in the system through access points(APs),this technology can meet the high transmission rate requirements of future wireless communications for 6G networks.In non-orthogonal multiple access(NOMA)system,multiple users share the same time-frequency resource block and successive interference cancellation(SIC)is used to eliminate interference among users,thereby improving spectral efficiency and increasing system capacity.This thesis applies NOMA to the cell-free massive MIMO system,fully utilizing the advantages of both spatial and power domain resources to better meet the requirements of future wireless communications for massive connections and high spectral efficiency.Specifically,this thesis conducts the following research on the cell-free massive MIMO-NOMA system:Firstly,this thesis studies a user clustering algorithm based on channel similarity for the SICbased cell-free massive MIMO-NOMA system.By clustering users with weak channel similarity into the same cluster using the proposed algorithm,the maximum correlation coefficient of the users’ channels within each cluster is minimized,and the interference between users in the clusters is reduced.The expression for the achievable uplink rate of the system’s users is derived theoretically under this model.At the same time,this thesis proposes a power optimization scheme aimed at minimizing the total power consumption of the system,and uses a parallel iterative algorithm to obtain the optimal power control scheme of the system.Simulation results demonstrate the feasibility of using this power control scheme in the SIC-based cell-free massive MIMO-NOMA system.Besides,the power consumption of the system under different user clustering schemes is compared and it is shown that using user clustering based on channel similarity can effectively reduce the power consumption of the system and improve the transmission performance of the system.Secondly,this thesis applies group successive interference cancellation(GSIC)to the cell-free massive MIMO-NOMA system,and a new method is developed to divide users into different groups accoding to their equivalent path loss,with space division multiple access(SDMA)transmission within each group and NOMA transmission between groups.GSIC is applied to sequentially decode each group signal,effectively reducing inter-user interference.To achieve minimum power consumption of the system,we convert the joint optimization problem into a pure power optimization problem by utilizing the relationship between the receiver and user’s transmit power coefficients.Furthermore,a combined inter-group and intra-group iterative approach is adopted to achieve the lowest power consumption for the system.Simulation results show that compared to the traditional cell-free massive MIMO-NOMA system based on SIC,the cell-free massive MIMO-NOMA system based on GSIC can significantly reduce inter-user interference,and lower the total power consumption of the system.