Leveraging Transformers for Enhanced Resource Allocation in Multi-Carrier NOMA Wireless Communication Systems

This study introduces an approach that utilizes a transformer model to enhance power allocation and channel assignment in multi-carrier non-orthogonal multiple access (NOMA) downlink systems, with the goal of maximizing the sum data rate. Designed to accommodate two users per channel, our method employs an encoder-only transformer to analyze the channel-gain-to-noise ratio (CNR) matrix and determine the optimal channel-selection matrix. We refine the optimization process with a custom loss function, tailored to the system’s specific constraints. To address the scalability challenges posed by large CNR matrices, we implement a dimensional expansion technique. This technique enables efficient training on downscaled simulated data without altering the transformer’s core architecture. Simulation results confirm the effectiveness of our strategy, showing over 90% accuracy in identifying optimal channel assignments and highlighting the potential of integrating transformers and deep learning into dynamic resource allocation for wireless communication systems.