This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The original optimization problem is prohibitively complicated when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, an im-proved K-means algorithm is proposed for user clustering, where the clustering metric considers both channel gain and channel correlation among users to reduce the intra- and inter-cluster interference. With this basis, the optimal cluster number and cluster centers are dynamically obtained by the semi-orthogonal user selection (SUS) algorithm. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved iteratively. Simulation results show that the proposed scheme achieves the near-optimal performance in terms of power consumption and energy efficiency with low computational complexity.