3 research outputs found
An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
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.Comment: 6 pages, 4 figures, conferenc
On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection
This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment