  Deep neural networks (NNs) have exhibited considerable potential for
efficiently balancing the performance and complexity of multiple-input and
multiple-output (MIMO) detectors. We propose a receiver framework that enables
efficient online training by leveraging the following simple observation:
although NN parameters should adapt to channels, not all of them are
channel-sensitive. In particular, we use a deep unfolded NN structure that
represents iterative algorithms in signal detection and channel decoding
modules as multi layer deep feed forward networks. An expectation propagation
(EP) module, called EPNet, is established for signal detection by unfolding the
EP algorithm and rendering the damping factors trainable. An unfolded turbo
decoding module, called TurboNet, is used for channel decoding. This component
decodes the turbo code, where trainable NN units are integrated into the
traditional max-log-maximum a posteriori decoding procedure. We demonstrate
that TurboNet is robust for channels and requires only one off-line training.
Therefore, only a few damping factors in EPNet must be re-optimized online. An
online training mechanism based on meta learning is then developed. Here, the
optimizer, which is implemented by long short-term memory NNs, is trained to
update damping factors efficiently by using a small training set such that they
can quickly adapt to new environments. Simulation results indicate that the
proposed receiver significantly outperforms traditional receivers and that the
online learning mechanism can quickly adapt to new environments. Furthermore,
an over-the-air platform is presented to demonstrate the significant robustness
of the proposed receiver in practical deployment.
