Fast Adaptive Intelligent Beam Training Method for mmWave Networks

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2024-09-24 DOI:10.1109/LCOMM.2024.3467100

Ziyan Lin;Jiamin Li;Pengcheng Zhu;Dongming Wang

引用次数: 0

Abstract

In this study, we consider a downlink millimeter wave (mmWave) transmission model with the objective of efficiently reducing beam training overhead and maximizing long-term average spectral efficiency. We propose a fast adaptive intelligent beam training algorithm based on a model-agnostic meta-reinforcement learning framework to interactively extract statistical information from mmWave environments and promptly detect beam failure by leveraging the spatial sparsity of mmWave channels. Simulation results demonstrate that the proposed algorithm exhibits rapid adaptability to dynamic communication environments and significantly enhances the spectral efficiency compared to existing algorithms.

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毫米波网络的快速自适应智能波束训练方法

在本研究中，我们考虑了一种下行毫米波（mmWave）传输模型，目的是有效减少波束训练开销，最大限度地提高长期平均频谱效率。我们提出了一种基于模型无关元强化学习框架的快速自适应智能波束训练算法，以交互方式从毫米波环境中提取统计信息，并利用毫米波信道的空间稀疏性及时发现波束故障。仿真结果表明，与现有算法相比，拟议算法能快速适应动态通信环境，并显著提高频谱效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.