Novel Design of User Scheduling and Analog Beam Selection in Downlink Millimeter-Wave Communications

Abstract

In this article, the joint design for user scheduling and analog beam selection in a downlink multiuser millimeter-wave (mmWave) system is studied. Our objective is to maximize the achievable sum rate under the user scheduling constraint, the analog beam selection constraint, and the resource capacity constraint. This problem is nonconvex and NP hard. We first propose a whale optimization algorithm (WOA)-based scheme to obtain a near-global-optimal solution with fast convergence and low complexity. Since the joint optimization user scheduling and beam selection problem is a constrained integer programming problem, the binary version of WOA is applied to deal with integer variables and the penalty method is used to handle the constraints. Besides, a nonlinear convergence factor is introduced to enhance the optimal solutions. For real-time use, we also propose a low-complexity machine-learning (ML)-based scheme. In the ML-based scheme, we decompose the original optimization problem into two subproblems: 1) user classification subproblem and 2) the analog beam selection subproblem. The user classification subproblem is solved based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm, where the users are clustered according to channel correlation. To solve the analog beam selection subproblem, we reformulate this subproblem as a multiclass classification problem. Considering the imbalance nature of the data set of the subproblem, we train the multiclass classifiers via the biased-SVM algorithm. Finally, the simulation results of the WOA-based scheme and the proposed ML-based scheme against the state-of-the-art schemes have shown the advantages of our proposed schemes.