Unsupervised Machine Learning Based User Clustering in mmWave-NOMA Systems

Millimeter-wave non-orthogonal multiple access (mmWave-NOMA) systems exploit the power domain for multiple access to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mmWave systems. This paper investigates the sum rate maximization problem of mmWave-NOMA systems under the constraints of the total transmission power and users’ predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users’ channels in mmWave-NOMA systems, we develop a K-means based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means based on-line user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mmWave-NOMA systems compared to the conventional user clustering algorithms; 2) the proposed K-means based on-line user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.