A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code(STBC)based multiple-input multiple-output(MIMO)systems.We exploit the zero-forcing equali...A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code(STBC)based multiple-input multiple-output(MIMO)systems.We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test(ALRT)function.The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification.The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information(CSI).Performance analysis is carried out for scenarios with different numbers of antennas.Alamouti-STBC systems with 2×2 and 2×1 and space-time transmit diversity with a 4×4 transmit and receive antenna configuration are considered to verify the proposed approach.Some popular modulation schemes are used as the modulation test pool.Monte-Carlo simulations are performed to evaluate the proposed methodology,using the probability of correct classification as the criterion.Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.展开更多
针对Alamouti空时块编码复用增益损失的问题,提出了两组Alamouti编码方案。在此基础上,为了改善系统的误码率(BER)性能和简化接收端复杂度,提出了将几何均值分解(GMD)算法和非线性预编码技术相结合的两组Alamouti传输方案。该方案的设...针对Alamouti空时块编码复用增益损失的问题,提出了两组Alamouti编码方案。在此基础上,为了改善系统的误码率(BER)性能和简化接收端复杂度,提出了将几何均值分解(GMD)算法和非线性预编码技术相结合的两组Alamouti传输方案。该方案的设计方法为:首先等效出两组Alamouti空时块编码系统的信道矩阵;进而,通过GMD算法对等效信道矩阵进行收发端联合设计;最后,在发射端应用脏纸(dirty paper coding,DPC)和Tomlinson-Harashima precoding(THP)非线性预编码技术,消除发送信号间的干扰,从而使系统获得更好的误码率性能。通过仿真结果对比发现,提出的系统可以显著地改善误码率性能。展开更多
考虑使用深度神经网络(DNN)开发一种决策导向(DD)信道估计(CE)算法,用于高度动态车辆环境中的MIMO空时块编码系统。Mehrtash Mehrabi等人2019年11月在《IEEE Journal on Selected Areas in Communications》发表文章,提出利用DNN进行空...考虑使用深度神经网络(DNN)开发一种决策导向(DD)信道估计(CE)算法,用于高度动态车辆环境中的MIMO空时块编码系统。Mehrtash Mehrabi等人2019年11月在《IEEE Journal on Selected Areas in Communications》发表文章,提出利用DNN进行空时块码(STBC)k步信道预测,并证明了基于深度学习(DL)的DD-CE算法不再需要估计快速时变准静态信道中随包不断变化的多普勒调频率。在这种车载信道中,估计多普勒频率的挑战性很大,需要大量的导频和前导码,从而导致功率和频谱效率较低。作者训练了两个深度神经网络,它们学习多普勒调频率范围很大的情况下MIMO衰落信道的实部和虚部。训练证明,通过以上深度神经网络,仅需多普勒调频率范围的先验知识,而无需知道其确切值,就可实现DD-CE。展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61172078,61571224,and 61571225)Six Talent Peaks Pro ject in Jiangsu Province,China.
文摘A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code(STBC)based multiple-input multiple-output(MIMO)systems.We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test(ALRT)function.The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification.The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information(CSI).Performance analysis is carried out for scenarios with different numbers of antennas.Alamouti-STBC systems with 2×2 and 2×1 and space-time transmit diversity with a 4×4 transmit and receive antenna configuration are considered to verify the proposed approach.Some popular modulation schemes are used as the modulation test pool.Monte-Carlo simulations are performed to evaluate the proposed methodology,using the probability of correct classification as the criterion.Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.
文摘针对Alamouti空时块编码复用增益损失的问题,提出了两组Alamouti编码方案。在此基础上,为了改善系统的误码率(BER)性能和简化接收端复杂度,提出了将几何均值分解(GMD)算法和非线性预编码技术相结合的两组Alamouti传输方案。该方案的设计方法为:首先等效出两组Alamouti空时块编码系统的信道矩阵;进而,通过GMD算法对等效信道矩阵进行收发端联合设计;最后,在发射端应用脏纸(dirty paper coding,DPC)和Tomlinson-Harashima precoding(THP)非线性预编码技术,消除发送信号间的干扰,从而使系统获得更好的误码率性能。通过仿真结果对比发现,提出的系统可以显著地改善误码率性能。
文摘考虑使用深度神经网络(DNN)开发一种决策导向(DD)信道估计(CE)算法,用于高度动态车辆环境中的MIMO空时块编码系统。Mehrtash Mehrabi等人2019年11月在《IEEE Journal on Selected Areas in Communications》发表文章,提出利用DNN进行空时块码(STBC)k步信道预测,并证明了基于深度学习(DL)的DD-CE算法不再需要估计快速时变准静态信道中随包不断变化的多普勒调频率。在这种车载信道中,估计多普勒频率的挑战性很大,需要大量的导频和前导码,从而导致功率和频谱效率较低。作者训练了两个深度神经网络,它们学习多普勒调频率范围很大的情况下MIMO衰落信道的实部和虚部。训练证明,通过以上深度神经网络,仅需多普勒调频率范围的先验知识,而无需知道其确切值,就可实现DD-CE。
