An Overview of Deep Learning for Resource Management in mmWave-NOMA - Redi Ramli; Byung Moo Lee (IEEE Access)
Millimeter-wave (mmWave) frequencies ranging from 30 to 300 GHz offer vast bandwidth and high data transmission rates, making them ideal for high-throughput applications and the expanding Internet of Things (IoT). However, mmWave implementation faces challenges such as narrow beams, susceptibility to blockage, and rapid channel fluctuations due to user mobility. To address these issues, non-orthogonal multiple access (NOMA) is employed, enhancing spectral efficiency by allowing multiple users to share the same frequency resources at different power levels. This paper focuses on Power-Domain NOMA (PD-NOMA), a variant of NOMA that allocates different power levels to users sharing the same frequency resources. Although other forms of NOMA, such as Code-Domain NOMA (CD-NOMA) and Cooperative NOMA, exist, our discussion will primarily focus on PD-NOMA due to its practical application in mmWave-NOMA networks. The integration of mmWave and NOMA presents both significant opportunities and complex challenges, particularly in resource management. This combination aims to leverage mmWave’s high bandwidth and NOMA’s efficient resources utilization to overcome physical layer limitations and enhance network performance. Traditional methods struggle with optimizing those resources like power levels, bandwidth, beam directions, and user pairing. Deep learning (DL) presents a promising solution by learning optimal resource allocation from data. This paper reviews current and future DL applications in five key areas: power allocation, energy efficiency, user association, bandwidth allocation, and subcarrier allocation in mmWave-NOMA networks. It also highlights available open-source datasets, code, and DL frameworks supporting these developments and discusses key research directions. The structure of the paper is organized as follows: first, background knowledge on mmWave and NOMA systems is presented, followed by an in-depth review of DL applications for resource management. Finally, we conclude with challenges and future research directions in this evolving field.