  In this work, we design a generative artificial intelligence (GAI) -based
framework for joint resource allocation, beamforming, and power allocation in
multi-cell multi-carrier non-orthogonal multiple access (NOMA) networks. We
formulate the proposed problem as sum rate maximization problem. Next, we
design a novel multi-task transformer (MTT) framework to handle the problem in
real-time. To provide the necessary training set, we consider simplified but
powerful mathematical techniques from the literature. Then, we train and test
the proposed MTT. We perform simulation to evaluate the efficiency of the
proposed MTT and compare its performance with the mathematical baseline.
