MultiNet: deep unsupervised power control for industrial MU-MIMO networks

• Main Authors: Maiti, Ritabrata, Madhukumar, A. S., Tan, Ernest Zheng Hui
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Resource allocation; Deep learning; Unsupervised learning; Power control
• Online Access: https://hdl.handle.net/10356/175403
• Institution: Nanyang Technological University

This paper presents Multinet, an unsupervised deep learning (DL) approach for power allocation in industrial environments and IIoT applications. Multinet extends the previously proposed singular value decomposition network (SVDNet), which utilizes supervised DL to approximate the performance of the WMMSE algorithm. While SVDNet requires labeled data for training, limiting its scalability and generalization performance, in contrast, Multinet employs unsupervised DL to directly optimize the sum rate maximization objective function, eliminating the need for labeled datasets and improving training efficiency. Simulation studies are conducted to evaluate Multinet's performance in an industrial environment, utilizing parameters derived from measured large-scale fading characteristics of the industrial radio channel at 5200 MHz. The suitability of Multinet for industrial applications is thus assessed and numerical evaluations demonstrate that Multinet outperforms benchmark supervised and unsupervised DL-based power control schemes in terms of sum rate and energy efficiency.