Enhancing AI-Driven Software with NOMA, UVFA, and Dynamic Graph Neural Networks for Scalable Decision-Making

Abstract

Background Artificial intelligence (AI) has revolutionized businesses by allowing for real-time decision-making across multiple applications. However, ensuring effective resource management, adaptability, and low latency is difficult with AI-driven software. Non-Orthogonal Multiple Access (NOMA), Universal Vector Function Approximation (UVFA), and Dynamic Graph Neural Networks (DGNNs) are potential approaches.
Methods This work investigates the integration of NOMA, UVFA, and DGNNs with AI systems to increase performance. NOMA enhances resource allocation by allowing several users to use common channels. UVFA efficiently approximates complex functions, but DGNNs adapt dynamically to changing data structures to enable ongoing decision-making.
Objectives The goal is to assess the efficacy of integrating NOMA, UVFA, and DGNNs in improving AI-driven software, with an emphasis on optimizing resource allocation, achieving scalable function approximation, and enabling dynamic data handling to improve real-time decision-making and system flexibility.
Results The results show that the suggested integration enhances resource allocation efficiency, data processing speed, optimization accuracy, and adaptability while reducing errors significantly when compared to existing techniques. This framework offers a scalable and high-performance solution for AI systems operating in dynamic contexts.
Conclusion Integrating NOMA, UVFA, and DGNNs improves AI-driven software scalability and adaptability, making it ideal for real-time applications. The strategy successfully optimizes resource utilization, resulting in responsive decision-making in data-intensive, large-scale AI systems.