传感技术学报2024，Vol.37Issue(6)：985-996,12.DOI:10.3969/j.issn.1004-1699.2024.06.008
ResNet-UAN-AUD:基于深度学习的水声上行非正交多址通信系统活动用户检测方法
ResNet-UAN-AUD:An Active User Detection Method for Underwater Acoustic Uplink NOMA Communication System Based on Deep Learning
摘要
Abstract
Underwater acoustic networks(UANs)are the primary technical means of detecting unknown waters.Non-orthogonal multiple access(NOMA)is a novel communications technology that supports non-orthogonal resource allocation in the time,frequency,or space/code domains,which can effectively improve network capacity and user access,providing innovative solutions for performance and power-constrained UANs.Active user detection(AUD)is essential for the NOMA system to eliminate signal interference and improve re-ception performance.ResNet is a neural network based on residual module hopping connection,which solves the problem of gradient disappearance and network degradation in deep learning.A ResNet-based AUD detection scheme(ResNet-UAN-AUD)is proposed for a hydroacoustic uplink NOMA system.Firstly,the basic model of the hydroacoustic uplink NOMA network is established.Secondly,the mathematical characterization of the AUD problem is realised.Thirdly,the ResNet-UAN-AUD is developed.Finally,the experimental simulation of the proposed scheme is carried out.The results show that the performance of ResNet-UAN-AUD is close to that of the ac-tive user detection scheme based on the long short-term memory network(LSTM-UAN-AUD).The complexity is slightly higher than that of the active user detection method based on the convolutional neural network(CNN-UAN-AUD),which achieves the suboptimal objec-tive and fits the hydroacoustic uplink NOMA system.关键词
水声网络/深度学习/残差神经网络(ResNet)/活动用户检测/上行NOMA通信系统Key words
underwater acoustic network/deep learning/residual neural network(ResNet)/active user detection/uplink NOMA commu-nication system分类
计算机与自动化引用本文复制引用
王建平,陈光岚,冯启高,马建伟..ResNet-UAN-AUD:基于深度学习的水声上行非正交多址通信系统活动用户检测方法[J].传感技术学报,2024,37(6):985-996,12.基金项目
河南省科技计划项目(232102111128,222102320181,222102110011) （232102111128,222102320181,222102110011）
河南省高等学校青年骨干教师计划项目(2019GGJS172),河南省高等学校重点科研计划项目(23B520003) （2019GGJS172）
2021年度国家级大学生创新训练项目重点支持领域项目(202110467001) （202110467001）
2021年度新乡市重大专项(21ZD003) （21ZD003）
河南省重点研发专项(241111211800) （241111211800）
