论文标题
在多个通道模型下，基于正交-SGD的MIMO检测方法
An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
论文作者
论文摘要
在本文中，提出了基于正交的随机梯度下降（O-SGD）学习方法，以解决人工神经网络（ANN）辅助MIMO信号检测中固有的无线通道过度训练问题。我们的基本思想在于发现和开发当前训练时期与过去训练时期之间的训练样本正交性。与仅基于当前培训样本更新神经网络的常规SGD不同，O-SGD发现了当前的培训样本与历史培训数据之间的相关性，然后将神经网络与这些无关组件更新。网络更新仅发生在那些已识别的NULL子空间中。通过这种方式，神经网络可以理解和记住不同无线通道之间的不相关组件，因此对于无线通道变化更加可靠。通过我们的广泛计算机模拟以及与常规SGD方法的性能比较证实了这一假设。
In this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neural network (ANN)-assisted MIMO signal detection. Our basic idea lies in the discovery and exploitation of the training-sample orthogonality between the current training epoch and past training epochs. Unlike the conventional SGD that updates the neural network simply based upon current training samples, O-SGD discovers the correlation between current training samples and historical training data, and then updates the neural network with those uncorrelated components. The network updating occurs only in those identified null subspaces. By such means, the neural network can understand and memorize uncorrelated components between different wireless channels, and thus is more robust to wireless channel variations. This hypothesis is confirmed through our extensive computer simulations as well as performance comparison with the conventional SGD approach.
