Machine Learning for 6G Wireless Networks: Paradigms, Enablers, and the Pursuit of AI-Native Trustworthiness

The sixth generation (6G) of wireless communication systems is envisioned to be inherently AI-native, integrating intelligence into every network layer to support unprecedented capabilities, including terabit-per-second data rates, submillisecond latency, and pervasive sensing. This ambition re- quires managing extreme complexity introduced by revolutionary technologies such as Terahertz (THz) communication, Ultra- Massive MIMO (UM-MIMO), and Reconfigurable Intelligent Surfaces (RIS). Machine Learning (ML) is recognized as the computational backbone for this transformation, enabling adaptive, self-optimizing, and context-aware wireless environments that fundamentally redefine how networks operate. This paper presents a systematic review, mapping ML across three progressive integration paradigms: AI for Network (AI4NET), Network for AI (NET4AI), and AI as a Service (AIaaS). We detail ML’s pivotal role in enhancing the physical layer through deterministic Wireless Environment Control (WEC) and robust channel estimation using generative models. Furthermore, we elaborate on distributed intelligence architectures, such as Federated Learning (FL) and Split Learning (SL), which are essential for balancing high computational demands with data privacy and resource constraints in the emerging Computing Power Network (CPN). Finally, we argue that the core viability of 6G depends on embedding trustworthiness into its architecture, emphasizing the mandatory roles of Explainable AI (XAI) for operational accountability and Distributed Ledger Technology (DLT) for immutable data provenance.

Authors

M L Sharma

Published

Nov 30, 2025

Journal or venue

International Journal for Research in Applied Science and Engineering Technology

DOI

10.22214/ijraset.2025.75641

Paper type

journal article

Source

Crossref