ct to UE N. Therefore, the rate of UE n increases slower because 3 5G NOMA-Enabled Wireless Networks of stronger residual interference from UE k. For the identity matrix precoding method, the rate of UE n increases slower than the rate of UE k when their power increases individually because UE n has a strong residual interference from UE k. We can see that the proposed precoding design performs much better than the identity matrix precoding method because it is designed to optimize the UE sum rate. Figure 3.4 shows the sum rate of UE n and UE k as Pn changes when B = 0.65. We can see that the proposed precoding design performs much better than the identity matrix pre- coding method because it is designed to optimize the UE sum rate. 3.6 Summary In this chapter, we consider a downlink wireless network which jointly incorporates NOMA and MIMO. A sum rate optimization problem is formulated with error propagation in SIC. In order to solve the problem, we introduce the concept of equivalent channel and pro- pose a sequential solution which solves for precoding matrix by applying an iterative algo- rithm first. Then we investigate the impact of power allocation by two cases: small error propagation factor and large error propagation factor. Simulations are performed to verify the superiority of proposed precoding design and our analyses on power allocation with residual interference. NOMA in Relay and loT for 5G Wireless Networks 4.1 Outage Probability Study in a NOMA Relay System 4.1.1 Background In Chapters 2 and 3, we have shown NOMA in D2D underlaid MIMO networks and the SIC error propagation. Existing works also evaluated NOMA's performance under outage analysis. In [214], the authors analyzed the performance of NOMA theoretically and they concluded that the disparity, either from user channels or intentionally created by allocat- ing different power factor, can further be beneficial to the system performance. A similar conclusion was drawn from cognitive radio NOMA (CR-NOMA) in [45]. Outage probabil- ity is a met