Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

TL;DR

The work argues that AI-native wireless networks require causal reasoning to overcome data-driven AI limitations such as opacity, poor generalization, and high energy cost. It proposes a framework based on causal discovery, causal representation learning, and causal inference to enable interventions, counterfactuals, and Layered SCMs across THz beamforming, digital twins, semantic communications, ISAC, and network control. Key contributions include CGMs for dynamic environment modeling, causal MO-RL and causal MAB for real-time decisions, and ISAC-specific causal strategies to optimize nonlinear sensing-communication trade-offs. The proposed paradigm aims to deliver dynamic adaptability, intent management, resilience, and time-critical operation while reducing training burdens and energy consumption, with practical guidance for integrating causality into layer-wise network design and future 6G architectures.

Abstract

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.

Figures (4)

• Figure 1: Causal AI vision for next-generation wireless networks.
• Figure 2: Mapping a propagation environment to a causal diagram. For simplicity, the diagram shows the causal dependencies for a single path, while the discussions in the paper is revolved around a more general case, that encompassed multiple scattering elements and multiple channel paths.
• Figure 3: Structural causal model representing inherent causal relations among network variables.
• Figure 4: Simple example of a structural causal model.

Theorems & Definitions (5)

• Definition 1
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