Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era

2026-01-05

arXiv.org

AI Summary

This work addresses the challenges posed by the high-density, large-scale deployment of next-generation networks—such as 6G driven by generative AI and quantum computing—where uncertainties render traditional model-based optimization methods, which rely on precise models and incur high computational complexity, increasingly impractical. The paper systematically reviews and explores the application of stochastic optimization algorithms in both single-objective and multi-objective settings, integrating model-driven and learning-driven paradigms to effectively tackle core challenges including load balancing, energy efficiency, and spectral efficiency. It establishes, for the first time, a clear connection between stochastic optimization and next-generation networking, identifies eight key open problems, and proposes a novel, scalable, and efficient intelligent optimization framework to provide theoretical foundations and future research directions for 6G network design.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Combinatorial Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies.

Problem

• Next-Generation Networks
• Stochastic Optimization
• 6G
• Uncertainty
• Large-Scale Optimization

Innovation

• Stochastic Optimization
• Next-Generation Networks
• Multi-Objective Optimization
• Generative AI
• 6G