Unsupervised Machine Learning-Based User Clustering in Millimeter-Wave-NOMA Systems

Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-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 mm-wave-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 online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-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 mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.