A Survey on AI for 6G: Challenges and Opportunities

arXiv cs.AI / 4/6/2026

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Key Points

The paper surveys how AI and machine learning can help shape 6G networks, targeting high data rates, low latency, and ubiquitous connectivity for applications such as smart cities, autonomous systems, holographic telepresence, and the tactile internet.
It highlights major AI techniques for 6G, including deep learning, reinforcement learning, federated learning, and explainable AI, and explains how these methods can integrate with core network functions.
It examines cross-cutting challenges for AI-enabled 6G—especially scalability, security, and energy efficiency—and proposes solution directions.
The survey connects AI-driven analytics to 6G service domains including URLLC, eMBB, mMTC, and ISAC, emphasizing how sensing and communications may be jointly optimized.
It also discusses broader concerns and future work areas around standardization, ethics, and sustainability, summarizing recent research trends for researchers and practitioners.

Abstract

As wireless communication evolves, each generation of networks brings new technologies that change how we connect and interact. Artificial Intelligence (AI) is becoming crucial in shaping the future of sixth-generation (6G) networks. By combining AI and Machine Learning (ML), 6G aims to offer high data rates, low latency, and extensive connectivity for applications including smart cities, autonomous systems, holographic telepresence, and the tactile internet. This paper provides a detailed overview of the role of AI in supporting 6G networks. It focuses on key technologies like deep learning, reinforcement learning, federated learning, and explainable AI. It also looks at how AI integrates with essential network functions and discusses challenges related to scalability, security, and energy efficiency, along with new solutions. Additionally, this work highlights perspectives that connect AI-driven analytics to 6G service domains like Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communication (mMTC), and Integrated Sensing and Communication (ISAC). It addresses concerns about standardization, ethics, and sustainability. By summarizing recent research trends and identifying future directions, this survey offers a valuable reference for researchers and practitioners at the intersection of AI and next-generation wireless communication.