  Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a
challenging problem for which traditional algorithms are either impractical or
suffer from performance limitations. Several recently proposed learning-based
approaches achieve promising results on simple channel models (e.g., i.i.d.
Gaussian). However, their performance degrades significantly on real-world
channels with spatial correlation. We propose MMNet, a deep learning MIMO
detection scheme that significantly outperforms existing approaches on
realistic channels with the same or lower computational complexity. MMNet's
design builds on the theory of iterative soft-thresholding algorithms and uses
a novel training algorithm that leverages temporal and spectral correlation to
accelerate training. Together, these innovations allow MMNet to train online
for every realization of the channel. On i.i.d. Gaussian channels, MMNet
requires two orders of magnitude fewer operations than existing deep learning
schemes but achieves near-optimal performance. On spatially-correlated
channels, it achieves the same error rate as the next-best learning scheme
(OAMPNet) at 2.5dB lower SNR and with at least 10x less computational
complexity. MMNet is also 4--8dB better overall than a classic linear scheme
like the minimum mean square error (MMSE) detector.
