Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing ``AI for wireless'' paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models, their curve-fitting nature, which can limit their ability to reason and adapt, their reliance on large amounts of training data, and the energy inefficiency of large neural networks. In response to these limitations, this article presents a comprehensive, forward-looking vision that addresses these shortcomings by introducing a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. Causal reasoning, founded on causal discovery, causal representation learning, and causal inference, can help build explainable, reasoning-aware, and sustainable wireless networks. Towards fulfilling this vision, we first highlight several wireless networking challenges that can be addressed by causal discovery and representation, including ultra-reliable beamforming for terahertz (THz) systems, near-accurate physical twin modeling for digital twins, training data augmentation, and semantic communication. We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges. Furthermore, we outline potential frameworks that leverage causal inference to achieve the overarching objectives of future-generation networks, including intent management, dynamic adaptability, human-level cognition, reasoning, and the critical element of time sensitivity.

翻译：尽管下一代无线网络（如6G）的基本前提是人工智能（AI）原生，但迄今为止，大多数现有研究要么停留在定性层面，要么是对现有“AI赋能无线”范式的增量式扩展。事实上，由于数据驱动、训练密集型AI的局限性，创建AI原生无线网络面临重大技术挑战。这些局限性包括AI模型的黑箱特性、其曲线拟合本质（可能限制其推理和自适应能力）、对大量训练数据的依赖，以及大型神经网络的能效低下问题。针对这些局限，本文提出了一种全面且前瞻性的愿景，通过引入基于新兴因果推理领域构建AI原生无线网络的新框架来解决上述不足。因果推理以因果发现、因果表征学习和因果推断为基础，有助于构建可解释、具备推理意识且可持续的无线网络。为实现这一愿景，我们首先强调了若干可通过因果发现和表征解决的无线网络挑战，包括太赫兹（THz）系统的超可靠波束赋形、数字孪生的近精确物理孪生建模、训练数据增强以及语义通信。我们展示了如何通过引入因果发现来增强动态适应性、鲁棒性和认知能力以应对这些挑战。此外，我们概述了潜在框架，利用因果推断实现未来网络的核心目标，包括意图管理、动态适应性、人类级认知、推理以及时间敏感性这一关键要素。