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

TL;DR

This paper introduces a generative AI framework using a multi-task transformer for real-time joint resource management in multi-cell multi-carrier NOMA networks, aiming to maximize sum rate.

Contribution

It presents a novel multi-task transformer model specifically designed for real-time resource allocation in complex NOMA networks, integrating AI with wireless communication optimization.

Findings

MTT outperforms baseline methods in sum rate maximization

Real-time resource management achieved with high efficiency

Simulation results validate the effectiveness of the proposed approach

Abstract

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.