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.
作者
陈扬钊
袁伟娜
CHEN Yang-zhao;YUAN Wei-na(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
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)。
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
massive machine type communication
grant-free transmission
non-orthogonal multiple access
compressed sensing
deep neural network
