Joint Beamforming and Power Optimization With Iterative User Clustering for MISO-NOMA Systems

Abstract

In this paper, we minimize the transmit power for multiple-input single-output and nonorthogonal multiple access systems. In our analysis, a large number of users are partitioned into multiple user clusters/pairs with small size and uniform power allocation across the clusters and each cluster is associated with a beamforming vector. The considered optimization problem involves how to optimize beamforming vectors, power allocation, and user clustering. Considering the high computational complexity in solving the whole problem, we decompose the problem into two parts, and design a joint algorithm to iteratively optimize them. First, given a user partition, we formulate the beamforming and power allocation problem under a set of practical constraints. The problem is nonconvex. To tackle it, we reformulate, transform, and approximate the nonconvex problem to a quadratically constrained optimization problem, and develop a joint beamforming and power allocation algorithm based on semidefinite relaxation to solve it. Second, to address the issue of high complexity in obtaining the optimal clusters, we propose a low-complexity algorithm to efficiently identify a set of promising clusters, forming as a candidate user partition. Based on these two algorithms, we design an algorithmic framework to iteratively perform them and to improve performance. By the algorithm design, the produced user partition can be further improved in later iterations, in order to further reduce power consumption. Numerical results demonstrate that the performance of the proposed solution with iterative updates for user clustering, and joint beamforming and power allocation optimization outperforms that of previous schemes.