联合射线追踪和神经网络的智慧教室环境信道特性研究  

Research on Channel Characteristics Under Smart Classroom Based on Ray Tracing and Neural Network

作　　者：黄蓉 余雨 HUANG Rong;YU Yu(Information construction and Management Office,Nanjing University of Posts and Telecommunications,Nanjing 210000,China;School of Information and Communication Engineering,Nanjing Institute of Technology,Nanjing 210000,China)

机构地区：[1]南京邮电大学信息化建设与管理办公室,江苏南京210000 [2]南京工程学院信息与通信工程学院,江苏南京210000

出　　处：《无线通信技术》2024年第2期33-38,共6页Wireless Communication Technology

基　　金：江苏省研究生教育教学改革课题(JGKT22_C049);江苏省自然科学基金青年基金(BK20201044);中国高校产学研创新基金(2021FNA05002)。

摘　　要：智慧教室在现代化教学实践起着重要作用,该场景下无线通信链路的可靠性是保证教学质量的关键。智慧教室环境下,无线信号需要无缝覆盖,课桌椅摆放形式灵活,人员遮挡大量存在,建立能够准确描述这些特性的信道模型十分关键。因此本文联合射线追踪信道数据和多层神经网络,构建了支持多元输入和二元输出的智慧教室环境下的信道模型。研究结果表明,与传统模型相比,所提出模型具有更高的准确度。该模型可为智慧教室环境中的无线通信系统设计提供基础参考。Smart classrooms play an important role in modern teaching practice.In this scenario,the reliability of wireless communication links is the key to ensuring the quality of teaching.In the smart classroom environment,wireless signal needs seamless coverage,desks and chairs can be placed flexibly,and a large numbers of people might exits.It is very important to establish a channel model that can accurately describe these characteristics.Thus,this paper combines ray tracing channel data and multi-layer neural network to construct a channel model in a smart classroom en⁃vironment that supports multiple inputs and binary outputs.The results show that the proposed model achieves higher accuracy than conventional models.The proposed model can provide basic references for the design of wireless communication systems in the smart classroom environment.

关 键 词：智慧教室 射线追踪 多层神经网络 信道特性 

分 类 号：TN915.08[电子电信—通信与信息系统]