ims to minimize interference among cellular users that coordinate with each other through NOMA and MU-MIMO beamforming. Beamforming is designed together with NOMA pairing and power allocation to significantly improve overall system sum throughput. Compared with its OMA counterpart, NOMA has a superior performance in terms of spectral efficiency. However, multiuser detection (MUD) is required at the receiver side, which induces a more complex receiver structure and algorithm. In [45], the impact of user pairing on the performance of both fixed power allocation and cognitive radio (CR) inspired NOMA (CR-NOMA) is studied. For the fixed one, NOMA tends to pair users with larger channel gain difference. In [215], a genetic algorithm (GA)-based NOMA pairing in the HetNet is presented, where GA will help reduce the computation workload. NOMA-Aided Network with Underlaid D2D We consider a downlink MU-MIMO cellular network that jointly supports NOMA, MU-MIMO, and underlying DUs. M CUs are randomly distributed, each equipped with 2.2 NOMA-Aided Network with Underlaid D2D Beam N Beam 1 CU1.1 N Antennas D2D Link 1 D2D Link P D2D TX D2D Link 2 D2D RX Figure 2.1 System model. a single antenna [179]. Each BS has N antennas and thus can maximally generate N beamforming vectors. Each beam can support multiple single antenna users by using NOMA, compared with one user in the conventional MU-MIMO system. Furthermore, a total number of P underlaid DU pairs, denoted as DU1, DU2, DUP, are also randomly distributed, to further exploit current spectrum reuse (Figure 2.1). For beam n, NOMA allows a setofPn={u(n,1),u(n,2),...,u(n,K)} n CUs to be scheduled on the same radio resource simultaneously, K>2. We use u(n, k) to denote the CU that is served by beam n with NOMA sequence k in that beam. Assume Xn is the transmitted signal in the n-th beam, and according to NOMA, Xn is a superimposed signal of a total K users in beam n, (2.1) Here E(Su(n.k)l , (E(.) is the expectation function. Nu(n,k) is the fraction of the allocated power to user =