  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.
