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，大多数现有工作仍停留在对现有“AI for Wireless”范式的定性或增量扩展层面。事实上，创建AI原生无线网络面临重大技术挑战，这源于数据驱动、训练密集的AI技术的局限性。这些局限包括AI模型的黑箱特性、其曲线拟合本质（可能限制推理和适应能力）、对大量训练数据的依赖，以及大型神经网络的低能效。针对这些局限，本文提出了一项全面且前瞻性愿景，通过引入基于因果推理新兴领域的创新框架来构建AI原生无线网络。因果推理建立在因果发现、因果表征学习和因果推断基础上，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，我们首先强调了几项可通过因果发现与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、数字孪生的高精度物理孪生建模、训练数据增强及语义通信。我们展示了引入因果发现如何助力在解决这些挑战时实现动态适应性、弹性及认知能力。此外，我们概述了利用因果推断实现未来网络总体目标的潜在框架，包括意图管理、动态适应性、人类级认知、推理及关键的时间敏感性要素。