# Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networks

arXiv: https://arxiv.org/abs/2405.05531 · HTML: https://mlsanity.com/paper/2405.05531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2024.

## Built on by (most cited first, 1 of 1)

- [Generative Diffusion Models for Resource Allocation in Wireless Networks](https://mlsanity.com/paper/2504.20277/) (2025)

## Abstract (arXiv)

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.