文献类型：期刊文献

中文题名：深度学习辅助上行免调度NOMA多用户检测方法

英文题名：Deep learning aided multi-user detection for up-link grant-free NOMA

作者：陈扬钊[1];袁伟娜[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2022

卷号：56

期号：4

起止页码：816

中文期刊名：浙江大学学报（工学版）

外文期刊名：Journal of Zhejiang University：Engineering Science

收录：CSTPCD;;EI(收录号：20221712042575);Scopus;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：国家自然科学基金资助项目(61501187)。

语种：中文

中文关键词：大规模机器通信;免调度传输;非正交多址接入;压缩感知;深度神经网络

外文关键词：massive machine type communication;grant-free transmission;non-orthogonal multiple access;compressed sensing;deep neural network

摘要：针对上行免调度非正交多址接入(NOMA)场景中多用户检测的问题,通过结合传输数据的符号特征,提出基于深度神经网络(DNN)的联合活跃用户检测和数据检测框架.考虑更一般化的实际场景,即用户在每个时隙中随机活跃.将DNN求解结果作为改进的正交匹配追踪(OMP)算法先验输入,修正提升活跃用户检测和数据检测性能.仿真结果表明,提出的多用户检测方案比传统的贪婪追踪及动态压缩感知(DCS)多用户检测算法具有更好的用户活跃性及数据检测性能.
A joint active user detection and data detection framework based on deep neural network(DNN)was proposed by combining the symbolic features of transmitted data in order to solve the problem of multi-user detection in uplink grant-free non-orthogonal multiple access(grant-free NOMA).The more general and practical scenario was considered,in which the user was randomly active in each time slot.The DNN solution result was used as a priori input of the modified orthogonal matching pursuit(OMP)algorithm in order to improve the user detection and date detection performance.The simulation results show that the proposed multi-user detection scheme has better user activity and data detection performance than the traditional greedy tracking algorithm and dynamic compressed sensing(DCS)multi-user detection algorithm.