摘要
针对通信信道中存在噪声等干扰因素时,QPSK接收机解调接收信号性能较差的问题,文章研究了一种基于深度学习的QPSK智能接收机模型;该QPSK智能接收机模型由LSTM神经网络和全连接层构成,借助了递归神经网络中的内存结构,也利用了LSTM能提取接收信号的时间相关性这一特点;在信噪比为0~7 dB的条件下进行仿真实验,实验结果表明,在加性高斯白噪声,同相和正交失衡以及频率偏差干扰因素影响下,文章研究的QPSK智能接收机模型在0~7 dB时的误码率相比于使用传统硬判决方法的通信接收机的误码率得到了显著降低;其中,QPSK智能接收机模型在7 dB时的误码率低至0.0109%,大约只有传统硬判决方法误码率的1/7;在发生频偏及同相和正交时,QPSK智能接收机模型在7 dB时的误码率分别低至0.0147%和0.0198%,都远低于相同条件下传统硬判决方法的误码率;因此,采用研究出来的QPSK智能接收机模型能够显著提高接收机的检测性能。
Aimed at the interference factors of noise in communication channels and poor performance of quadrature phase shift keying(QPSK)receiver demodulating received signals.In view of the problem.a QPSK intelligent receiver model based on deep learning is studied.The QPSK intelligent receiver model is composed of the long and short-term memory(LSTM)neural network and fully connected layer.With the help of the memory structure in the recurrent neural network,LSTM is also used to extract the characteristic of the temporal correlation for the received signal.Simulation experiments is conducted under the condition of a signal-to-noise ratio of 0 to 7 dB,the experimental results show that under the influences of Gaussian white noise,in-phase and quadrature imbalance,and frequency deviation interference factors,and compared with the traditional hard decision method,the bit error rate of the proposed QPSK intelligent receiver model with the signal-to-noise ratio of 0 to 7 dB is significantly reduced.Among them,the bit error rate of QPSK intelligent receiver model at the signal-to-noise ratio of 7 dB is as low as 0.0109%,which is only about 1/7 of the bit error rate of the traditional hard decision method.In the conditions of frequency deviation and IQ imbalance,the bit error rate of QPSK intelligent receiver model at the signal-to-noise ratio of 7 dB is as low as 0.0147%and 0.0198%,respectively,which are much lower than the bit error rate of the traditional hard decision method under the same condition.Therefore,the proposed QPSK intelligent receiver model can significantly improve the detection performance of the receiver.
作者
朱力
韩会梅
彭宏
ZHU Li;HAN Huimei;PENG Hong(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机测量与控制》
2024年第2期213-218,共6页
Computer Measurement &Control
基金
国家自然科学基金(62001419)。
