  Deep learning has solved many problems that are out of reach of heuristic
algorithms. It has also been successfully applied in wireless communications,
even though the current radio systems are well-understood and optimal
algorithms exist for many tasks. While some gains have been obtained by
learning individual parts of a receiver, a better approach is to jointly learn
the whole receiver. This, however, often results in a challenging nonlinear
problem, for which the optimal solution is infeasible to implement. To this
end, we propose a deep fully convolutional neural network, DeepRx, which
executes the whole receiver pipeline from frequency domain signal stream to
uncoded bits in a 5G-compliant fashion. We facilitate accurate channel
estimation by constructing the input of the convolutional neural network in a
very specific manner using both the data and pilot symbols. Also, DeepRx
outputs soft bits that are compatible with the channel coding used in 5G
systems. Using 3GPP-defined channel models, we demonstrate that DeepRx
outperforms traditional methods. We also show that the high performance can
likely be attributed to DeepRx learning to utilize the known constellation
points of the unknown data symbols, together with the local symbol
distribution, for improved detection accuracy.
