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In this paper, an orthogonal stochastic gradient descent (O-SGD) basedlearning approach is proposed to tackle the wireless channel over-trainingproblem inherent in artificial neural network (ANN)-assisted MIMO signaldetection. Our basic idea lies in the discovery and exploitation of thetraining-sample orthogonality between the current training epoch and pasttraining epochs. Unlike the conventional SGD that updates the neural networksimply based upon current training samples, O-SGD discovers the correlationbetween current training samples and historical training data, and then updatesthe neural network with those uncorrelated components. The network updatingoccurs only in those identified null subspaces. By such means, the neuralnetwork can understand and memorize uncorrelated components between differentwireless channels, and thus is more robust to wireless channel variations. Thishypothesis is confirmed through our extensive computer simulations as well asperformance comparison with the conventional SGD approach.

An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models