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）的基本前提是具备原生人工智能（AI）能力，但迄今为止，大多数现有研究仍停留在对现有"AI赋能无线"范式的定性或渐进式拓展。事实上，由于数据驱动、训练密集型AI存在局限性——包括AI模型的黑箱特性、受曲线拟合本质限制的推理与自适应能力不足、对大量训练数据的依赖，以及大型神经网络能效低下等问题——构建原生AI无线网络面临重大技术挑战。针对这些局限，本文提出了一项全面且具有前瞻性的愿景，通过引入基于新兴因果推理领域的创新框架来构建原生AI无线网络。因果推理建立在因果发现、因果表征学习与因果推断的基础上，能够帮助构建可解释、具备推理意识且可持续发展的无线网络。为实现这一愿景，我们首先阐明了可通过因果发现与表征解决的若干无线组网挑战，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助力实现动态自适应、弹性与认知能力以应对这些挑战。此外，我们勾勒了利用因果推断实现未来一代网络总体目标的潜在框架，涵盖意图管理、动态自适应、人类级认知、推理能力以及关键的时间敏感性要素。