深度学习辅助上行免调度NOMA多用户检测方法  

Deep learning aided multi-user detection for up-link grant-free NOMA

作　　者：陈扬钊 袁伟娜[1] CHEN Yang-zhao;YUAN Wei-na(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《浙江大学学报（工学版）》2022年第4期816-822,共7页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61501187)。

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.

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 

分 类 号：TN929[电子电信—通信与信息系统]