Mixture of Experts (MoE) has emerged as a promising paradigm for scaling model capacity while preserving computational efficiency, particularly in large-scale machine learning architectures such as large language models (LLMs). Recent advances in MoE have facilitated its adoption in wireless networks to address the increasing complexity and heterogeneity of modern communication systems. This paper presents a comprehensive survey of the MoE framework in wireless networks, highlighting its potential in optimizing resource efficiency, improving scalability, and enhancing adaptability across diverse network tasks. We first introduce the fundamental concepts of MoE, including various gating mechanisms and the integration with generative AI (GenAI) and reinforcement learning (RL). Subsequently, we discuss the extensive applications of MoE across critical wireless communication scenarios, such as vehicular networks, unmanned aerial vehicles (UAVs), satellite communications, heterogeneous networks, integrated sensing and communication (ISAC), and mobile edge networks. Furthermore, key applications in channel prediction, physical layer signal processing, radio resource management, network optimization, and security are thoroughly examined. Additionally, we present a detailed overview of open-source datasets that are widely used in MoE-based models to support diverse machine learning tasks. Finally, this survey identifies crucial future research directions for MoE, emphasizing the importance of advanced training techniques, resource-aware gating strategies, and deeper integration with emerging 6G technologies.

翻译：专家混合模型（MoE）已成为一种在保持计算效率的同时扩展模型容量的有前景范式，尤其在大规模机器学习架构（如大语言模型）中表现显著。MoE的最新进展促进了其在无线网络中的应用，以应对现代通信系统日益增长的复杂性和异构性。本文对无线网络中的MoE框架进行全面综述，重点阐述其在优化资源效率、提升可扩展性以及增强跨多样化网络任务适应性方面的潜力。我们首先介绍MoE的基本概念，包括多种门控机制及其与生成式人工智能和强化学习的融合。随后，系统探讨MoE在关键无线通信场景中的广泛应用，例如车联网、无人机网络、卫星通信、异构网络、集成感知与通信以及移动边缘网络。此外，本文深入剖析了MoE在信道预测、物理层信号处理、无线资源管理、网络优化与安全等核心领域的具体应用。同时，详细概述了广泛应用于MoE模型的开源数据集，这些数据集为多样化机器学习任务提供了重要支持。最后，本综述明确了MoE未来关键的研究方向，强调先进训练技术、资源感知型门控策略以及与新兴6G技术深度融合的重要性。