n model in a downlink MIMO NOMA system, where decoding errors are modeled as residual interference. An optimization problem is formulated to maximize the total data rate of two users. 3.2 Error Propagation in NOMA We consider a downlink wireless communication which jointly supports NOMA and MU-MIMO. In the system, a BS with power PBS is equipped with M antennas. Two user equipments (UEs) are randomly deployed in this area, each has N antennas [172]. Due to the usage of NOMA, two UEs can receive signals from the BS simultaneously. Besides, the BS is assumed to have an accurate CSI of UEs based on training sequences and feedback mechanism. We denote Hk and Hn (both with dimension CNXM) as the channel gain matrix of UE k and UE n, respectively. hij is the element from ith row and jth col- umn in the matrix, and it is modeled as the product of large-scale path loss and fading, i.e. hij = laho, where lij is the distance between UE and BS, a is the path loss exponent, and ho is the Gaussian random variable with distribution ho~CN (0,1). The transmitted signal from the BS is: (3.1) where Wn and Wk are precoding matrices with dimension CMxN Xn and Xx E CNX1 are messages for UE n and UE k, respectively. E(x) = E(x x H ) = IN, E(.) is the expectation function and IN is a N x N identity matrix. The received signal at UE n is (3.2) Similarly, UE k will receive, (3.3) where is the i.i.d additive gaussian noise which follows CN(0,02IN). 3.3 SIC and Problem Formulation The key idea of SIC can be summarized as a process of decoding, reconstruction, and sub- traction (DRS). Upon the reception of the composite signal, DRS will start with the strongest user signal and treat others as interference. After the successful decoding, the data will re-encode based on user channel estimation and constellation. The reconstructed signal should be fairly close to the received signal if everything is perfect. Then, the user will sub- tract this signal from the aggregated signal SO that the next DRS will see less interference if the intended me