Synaptic Intelligence-Based Beam Selection in Dynamic Environments

IF 4.4 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-07-07 DOI:10.1109/LCOMM.2025.3586843

Yunwei Gou;Yawen Chen;Yifan Zhu;Wan Xiang;Zhaoming Lu;Xiangming Wen

引用次数: 0

Abstract

In real-world vehicular communications, machine learning-based beam selection is challenging under non-stationary distributions of Non-Line of Sight (NLOS) and Line of Sight (LOS) cases. For example, a model newly updated on rush-hour traffic hardly re-adapts to previously encountered regular traffic. To address this, the letter proposes a continual learning approach, named Synaptic Intelligence-based Beam Selection (SIBS), which retains historical knowledge by restricting the change to key parameters during the new training. The experiments on simulated datasets show strong adaptability of SIBS to dynamic environments, where it adapts to the new scenario and notably maintains performance over the encountered scenario.

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动态环境下基于突触智能的波束选择

在实际的车辆通信中，基于机器学习的波束选择在非平稳分布的非瞄准线（NLOS）和瞄准线（LOS）情况下具有挑战性。例如，新更新的高峰时段交通模型很难重新适应以前遇到的常规交通。为了解决这个问题，信中提出了一种持续学习的方法，称为突触智能波束选择（SIBS），该方法通过限制新训练期间关键参数的变化来保留历史知识。在模拟数据集上的实验表明，SIBS对动态环境具有较强的适应性，能够适应新的场景，并在遇到的场景中保持良好的性能。

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.