6 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
Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design
This work aims to handle the joint transmitter
and noncoherent receiver optimization for multiuser single-input
multiple-output (MU-SIMO) communications through unsupervised
deep learning. It is shown that MU-SIMO can be modeled
as a deep neural network with three essential layers, which
include a partially-connected linear layer for joint multiuser
waveform design at the transmitter side, and two nonlinear layers
for the noncoherent signal detection. The proposed approach
demonstrates remarkable MU-SIMO noncoherent communication
performance in Rayleigh fading channels