College of Engineering, Xi'an International University, Xi'an 710077, China.
College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Comput Intell Neurosci. 2019 Mar 4;2019:8613639. doi: 10.1155/2019/8613639. eCollection 2019.
A multiuser detection (MUD) algorithm based on deep learning network is proposed for the satellite mobile communication system. Due to relative motion between the satellite and users, multiple access interference (MUI) introduced by multipath fading channel reduces system performance. The proposed MUD algorithm based on deep learning network firstly establishes the CINR optimal loss function according to the multiuser access mode and then obtains the best multiuser detection weight through the steepest gradient iteration. Multilayer nonlinear learning obtains interference cancellation sharing weights to achieve maximum signal-to-noise ratio through gradient iteration, which is superior than the traditional serial interference cancellation algorithm and parallel interference cancellation algorithm. Then, the weights with multiuser detection through multilayer network forward learning iteration are obtained with traditional multiuser detecting quality characteristics. The proposed multiuser access detection based on deep learning network algorithm improves the MUD accuracy and reduces the number of traditional multiusers. The performance of the satellite multifading uplink system shows that the proposed deep learning network can provide high precision and better iteration times.
提出了一种基于深度学习网络的卫星移动通信系统多用户检测（MUD）算法。由于卫星和用户之间的相对运动，多径衰落信道引入的多址干扰（MUI）降低了系统性能。该算法基于深度学习网络，首先根据多用户接入模式建立 CINR 最优损失函数，然后通过最陡梯度迭代得到最佳多用户检测权重。多层非线性学习获得干扰消除共享权重，通过梯度迭代实现最大信噪比，优于传统的串行干扰消除算法和并行干扰消除算法。然后，通过传统的多用户检测质量特性获得通过多层网络前向学习迭代的多用户检测权重。基于深度学习网络的多用户接入检测算法提高了 MUD 的准确性并减少了传统多用户的数量。卫星多衰落上行链路系统的性能表明，所提出的深度学习网络可以提供高精度和更好的迭代次数。
