Resource Allocation in NOMA Networks: Convex Optimization and Stacking Ensemble Machine Learning

Abstract

This article addresses the joint power allocation and channel assignment (JPACA) problem in uplink non-orthogonal multiple access (NOMA) networks, an essential consideration for enhancing the performance of wireless communication systems. We introduce a novel methodology that integrates convex optimization (CO) and machine learning (ML) techniques to optimize resource allocation efficiently and effectively. Initially, we develop a CO-based algorithm that employs an alternating optimization strategy to iteratively solve for channel and power allocation, ensuring quality of service (QoS) while maximizing the system’s sum-rate. To overcome the inherent challenges of real-time application due to computational complexity, we further propose a ML-based approach that utilizes a stacking ensemble model combining convolutional neural network (CNN), feed-forward neural network (FNN), and random forest (RF). This model is trained on a dataset generated via the CO algorithm to predict optimal resource allocation in real-time scenarios. Simulation results demonstrate that our proposed methods not only reduce the computational load significantly but also maintain high system performance, closely approximating the results of more computationally intensive exhaustive search methods. The dual approach presented not only enhances computational efficiency but also aligns with the evolving demands of future wireless networks, marking a significant step towards intelligent and adaptive resource management in NOMA systems.