AI-Based Beam Management in 3GPP: Optimizing Data Collection Time Window for Temporal Beam Prediction

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-29 DOI:10.1109/OJVT.2023.3337357
Yingshuang Bai;Jiawei Zhang;Chen Sun;Le Zhao;Haojin Li;Xiaoxue Wang
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Abstract

Artificial Intelligence (AI) has gained significant attention and extensive research across various fields in recent years. In the realm of wireless communication, researchers are exploring the use of AI to facilitate various physical layer (PHY) procedures. Within the standardization efforts of the Third Generation Partnership Project (3GPP), one prominent direction being explored is AI-based beam management (BM). The primary objective is to harness AI techniques for predicting optimal beams, thereby reducing measurement overhead and latency. This paper aims to discuss the progress made in AI-based beam management within the Release 18 standardization. Furthermore, through our research, we have identified the mobile speed of user equipment (UE) as a crucial factor that impacts the optimal time window size for collecting input data in AI models. We have observed an inverse correlation between UE speed and the time window size. Accordingly, to mitigate unnecessary measurement overhead and latency, we propose that the determination of the time window size for input data collection should be based on the UE speed. Additionally, we will present our simulation results and provide a comprehensive analysis of this relationship.
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3GPP 中基于人工智能的波束管理:优化数据采集时间窗口以实现时域波束预测
近年来,人工智能(AI)在各个领域都获得了极大的关注和广泛的研究。在无线通信领域,研究人员正在探索使用人工智能来促进各种物理层(PHY)程序。在第三代合作伙伴计划(3GPP)的标准化工作中,一个突出的探索方向是基于人工智能的波束管理(BM)。其主要目标是利用人工智能技术预测最佳波束,从而减少测量开销和延迟。本文旨在讨论基于人工智能的波束管理在第 18 版标准化中取得的进展。此外,通过研究,我们发现用户设备(UE)的移动速度是影响人工智能模型中收集输入数据的最佳时间窗口大小的关键因素。我们观察到 UE 速度与时间窗口大小之间存在反相关关系。因此,为了减少不必要的测量开销和延迟,我们建议应根据 UE 速度来确定收集输入数据的时间窗口大小。此外,我们还将展示模拟结果,并对这种关系进行全面分析。
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CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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