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An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio

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arxiv 2308.05315 v1 pith:W463FXIM submitted 2023-08-10 cs.NI eess.SP

classification cs.NIeess.SP
keywords communicationinterfacewirelessartificialgenerationintelligencejourneyoverview
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Air interface is a fundamental component within any wireless communication system. In Release 18, the 3rd Generation Partnership Project (3GPP) delves into the possibilities of leveraging artificial intelligence (AI)/machine learning (ML) to improve the performance of the fifth-generation (5G) New Radio (NR) air interface. This endeavor marks a pioneering stride within 3GPP's journey in shaping wireless communication standards. This article offers a comprehensive overview of the pivotal themes explored by 3GPP in this domain. Encompassing a general framework for AI/ML and specific use cases such as channel state information feedback, beam management, and positioning, it provides a holistic perspective. Moreover, we highlight the potential trajectory of AI/ML for the NR air interface in 3GPP Release 19, a pathway that paves the journey towards the sixth generation (6G) wireless communication systems that will feature integrated AI and communication as a key usage scenario.

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Cited by 3 Pith papers

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  1. Data-Driven Cellular Mobility Management via Bayesian Optimization and Reinforcement Learning

    cs.IT 2025-05 conditional novelty 5.0 of 10

    Per-cell Bayesian optimization of handover parameters beats fixed 3GPP settings in a simulated urban network, while reinforcement learning matches it with transfer learning.

  2. AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks

    eess.SP 2026-08 conditional novelty 4.0 of 10

    A weighted feature-scoring and greedy selection framework chooses a compact set of drone and base-station beam links for ISAC localization, claiming a balanced accuracy-overhead tradeoff.

  3. Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN

    cs.NI 2025-05 conditional novelty 4.0 of 10

    A Jetson-based research kit aims to make real-time 5G NR AI-RAN prototyping practical for academic labs.

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