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.

翻译：下一代网络正从单纯的设备连接向构建互联智能生态系统演进，特别是在生成式人工智能与量子计算的支持下。这些系统需要处理大规模部署和具有多样化功能的高密度网络。因此，对能够在传播环境和网络系统双重不确定性下运行的高效智能算法的需求日益增长。传统优化方法通常依赖于精确的数据传输理论模型，但在现实世界的下一代网络场景中，它们在大规模设置下面临高计算复杂度的挑战。随机优化算法专为适应极高密度和广泛网络可扩展性而设计，已成为优化无线网络的有力解决方案，涵盖从基于模型的方法到基于学习的方法等多种类别。这些技术能够在可行的时间范围内收敛，同时处理复杂的大规模优化问题。然而，目前针对下一代网络（尤其是即将到来的第六代移动通信系统）应用随机优化的研究仍然有限。本综述通过八个涉及背景、关键特征和经验教训的开放性问题，重点阐释了下一代系统与随机优化之间的内在联系。总体而言，我们的研究首先对这两个领域进行了详细概述，涵盖了从单目标到多目标信号处理的基础性和广泛使用的随机优化技术。接着，我们探讨了不同算法如何解决下一代网络面临的挑战，例如负载均衡、优化能源效率、提升频谱效率或处理多重性能权衡。最后，我们指出了当前研究中的挑战，并为未来研究提出了新的方向。