as |hu(n,1) hu(n,2).<unk)Since the decoding order follows the ascending order of channel gains, CU j will decode CU i message, if i <j. SIC then removes the decoded message from its observation. CU i treats signals from CUs with index j> i as interference. Assuming perfect interference cancellation, we can rewrite (2.3) as Yuau = where the second term on the right side is the interference from users in the same NOMA group. The third term represents inter-beam interference. After applying SIC, the received signal-to-noise-plus-interference-ratio (SINR) Yu(n,k) of CU u(n, k) becomes (2.5) where (2.6) (2.7) 2.4 Precoding and User Grouping Algorithm (2.8) respectively represent SIC, inter-beam, and DU interference to CU u(n,k). Similarly, SINR YDU of the DU p is expressed as (2.9) Given SINR, the corresponding user data rate can be calculated as f(E{r}) by using Shannon capacity formula, f(E{r})=log(1+E{y}). (2.10) Here we normalize the bandwidth at MBS to 1. 2.3.2 Problem Formation The design objective is to maximize the total system sum throughput from both CUs and DUs. To this end, we need to determine (i) the NOMA set of each beam, i.e. ; (ii) the power allocation factor Nu(n,k) for each user k in the NOMA set of beam n; and (iii) the precoding vector Wn. Therefore, the problem can be formulated as follows. (2.11) subject to (2.12) (2.13) Wn E C NX1. (2.14) Constraint (2.12) is the summation of user power in one beam. Constraint (2.13) sets a lower rate limit for users that experience SIC interference in NOMA to ensure good user experience. Yu(n,k) and YDU are rates calculated based on (2.5) and (2.9), respectively. The optimization problem is a non-convex problem that needs to determine P n' Wn, >u(n,k) jointly. To make this problem feasible to solve, in Section 2.4, we seek a heuristic solution by decomposing the original problem into two sub-problems. We first develop different precoding methods, which aim to suppress either the inter-beam interference among CUs or the interference from CUs to DUs. Based on th