  Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of
the pivotal technologies for future wireless networks. However, the performance
of massive MIMO systems heavily relies on accurate channel estimation. While
the acquisition of channel state information (CSI) in such systems requires an
increasingly large amount of training overhead as the number of users grows. To
tackle this issue, in this paper, we propose a deep learning assisted blind
channel estimation technique for orthogonal frequency division multiplexing
(OFDM) based massive MIMO systems. We prove that by exploiting the asymptotic
orthogonality of the massive MIMO channels, the channel distortion can be
averaged out without the prior knowledge of channel impulse responses, and
after some mathematical manipulation, different users transmitted data symbols
can be extracted. Thus, by deploying a denoising convolutional neural network
algorithm (DnCNN), we mitigate a remaining channel and noise effect to
accurately detect the transmitted data symbols at the channel sounding stage.
Using the detected data symbols as virtual pilots, we estimate the CSI of all
the users at each BS antennas. Our simulation results testify the efficacy of
our proposed technique and demonstrate that it can provide a mean square error
(MSE) performance which coincides with that of the data-aided channel
estimation technique.
