摘要
文章主要引入深度学习算法优化有线宽带网络的通信性能。分析有线宽带网络中的带宽瓶颈、信号衰减和失真等问题对通信性能的不利影响,基于深度学习设计神经网络模型,并应用于流量预测和传输协议优化,旨在提高网络整体效率。通过精心设计的实验,详细评估了深度学习算法在有线宽带网络中优化通信性能的表现,并与传统方法进行对比研究。研究结果表明,深度学习在有线宽带网络中提升通信性能方面具有显著优势。
Deep learning algorithms are mainly introduced to optimize the communication performance in wired broadband networks.Analyzing the adverse effects of bandwidth bottlenecks,signal fading and distortion on communication performance in wired broadband networks,a neural network model is designed based on deep learning and applied to traffic prediction and transmission protocol optimization,aiming to improve the overall efficiency of the network.Through well-designed experiments,the performance of deep learning algorithms for optimizing communication performance in wired broadband networks is evaluated in detail and studied in comparison with traditional methods.The results show that deep learning has significant advantages in enhancing communication performance in wired broadband networks.
作者
谢庆助
XIE Qingzhu(China Mobile Guangxi Co.,Ltd.,Hezhou Branch,Hezhou 542899,China)
出处
《通信电源技术》
2024年第3期158-160,共3页
Telecom Power Technology
关键词
有线宽带网络
深度学习算法
通信性能优化
wired broadband networks
deep learning algorithms
communication performance optimization
