基于深度学习的通信原理实验系统设计与验证  

Design and Verification of Communication Principle Experimental System Based on Deep Learning

作　　者：付垚[1] FU Yao(School of Information Science and Engineering,Shenyang University of Technology,Shenyang,Liaoning 110159,China)

机构地区：[1]沈阳理工大学信息科学与工程学院,辽宁沈阳110159

出　　处：《自动化应用》2025年第6期64-66,69,共4页Automation Application

摘　　要：随着通信技术的发展,传统的通信原理实验教学面临诸多挑战,为此,设计并验证了一种基于深度学习的通信原理实验系统。该系统利用深度学习算法对信道建模、调制识别、信号检测等关键环节进行智能化处理,显著提升了低信噪比环境下的通信性能。实验结果表明,所设计的系统在误比特率和误帧率方面均优于传统方案。With the development of communication technology,traditional communication principle experimental teaching faces many challenges.Therefore,a deep learning based communication principle experimental system has been designed and verified.The system utilizes deep learning algorithms to intelligently process key processes such as channel modeling,modulation recognition,and signal detection,significantly improving communication performance in low signal-to-noise ratio environments.The experimental results show that the designed system outperforms traditional schemes in terms of bit error rate and frame error rate.

关 键 词：深度学习 通信原理 实验系统 信道建模 

分 类 号：TN911[电子电信—通信与信息系统]