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.

翻译：本文设计了一种基于生成式人工智能的框架，用于多小区多载波非正交多址接入（NOMA）网络中的联合资源分配、波束赋形与功率分配。我们将所提出的问题建模为和速率最大化问题，进而设计了一种新颖的多任务Transformer（MTT）框架以实时处理该问题。为提供必要的训练数据集，我们采用了文献中简化但高效的数学技术，随后对提出的MTT进行训练与测试。通过仿真实验评估所提MTT的性能，并将其与数学基线方法进行对比。