Optimization Framework for User Clustering, Beamforming Design and Power Allocation in MIMO-UFMC Systems

Universal Filtered Multi-Carrier (UFMC) is a new multi-carrier system regarded as a candidate for future wireless communication systems. This paper investigates the application of the UFMC technique in Multiple Input-Multiple Output communications (MIMO-UFMC), in which is shown that the considered structure of the transceiver leads to the removal of intra-group interference. We first propose a user grouping algorithm based on an unsupervised machine learning algorithm (Generalized K-means clustering). Whereafter, the beamforming matrix is designed based on the Zero-Forcing (ZF) method and signal-to-interference maximization. Then, the problem of maximizing the achievable sum rate is formulated as a non-convex optimization. To solve this problem, we first exploit Difference-Convex Programming (DCP) as an efficient numerical solution. In the following, some relaxations are expressed to simplify the formulated problem and then solve it based on the convex optimization methods and the Ellipsoid algorithm. Simulation results show that the proposed schemes, including user grouping, beamforming design, and power allocation, outperform the conventional benchmark schemes.