Machine Learning-Enabled RIS-Assisted MmWave NOMA Systems: RIS Partitioning, Beamforming Design, and Power Allocation

• Published in: IEEE transactions on communications p. 1
• Main Authors: Wei, Tongyi, Chen, Weizhi, Tang, Kun, Zheng, Beixiong, Feng, Wenjie, Che, Wenquan, Xue, Quan
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Array signal processing; Clustering algorithms; Job shop scheduling; machine learning (ML); Machine learning algorithms; Millimeter wave communication; millimeter-wave; NOMA; non-orthogonal multiple access (NOMA); Optimization; Partitioning algorithms; Reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Resource management

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication system is considered, where the RIS is virtually partitioned into several sub-RISs to serve different user clusters. We aim to maximize the sum achievable r...