Orthogonal Multiple Access

Orthogonal Multiple Access (OMA) traditionally assigns orthogonal resources to users, limiting capacity. Current research focuses on Non-Orthogonal Multiple Access (NOMA), which allows multiple users to share resources, improving spectral efficiency but introducing interference challenges. Researchers are exploring solutions using machine learning techniques like graph neural networks and deep reinforcement learning for efficient resource allocation and interference mitigation, as well as employing novel waveform designs and signal processing methods to enhance performance in diverse applications such as the Internet of Things and 5G/6G communication systems. These advancements aim to improve data rates, reduce latency, and enhance the overall efficiency of wireless communication networks.