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.
Tab.1Schematic table of transmission symbol feature mapping
参数
参数值
扩频序列长度
100
接入用户数
200
稀疏度
20
调制方式
QPSK
DNN层数
5
学习率
0.01
训练数据量
320000
批量大小
2 000
$ C $
200
蒙特卡洛仿真次数
100
Tab.2Simulation parameter table of Grant-free NOMA system
Fig.2BER performance comparison under different SNR
Fig.3BER performance of different algorithms under different SNR when users are randomly active
Fig.4Comparison of BER performance of different algorithms under 300% load conditions
Fig.5BER performance comparison against sparsity level
Fig.6Probability of successful reconstruction of different algorithms under different SNR when users are randomly active
多用户检测算法
$t_{\rm{a}} $/s
LS
0.004 9
OMP
0.063 9
CoSaMP
0.615 7
SP
0.455 8
DCS
0.179 9
DPA-OMP中改进的OMP算法部分
0.048 5
DPA-OMP整体
0.253 5
Tab.3Simulation running time of different multi-user detection algorithms
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