  A canonical wireless communication system consists of a transmitter and a
receiver. The information bit stream is transmitted after coding, modulation,
and pulse shaping. Due to the effects of radio frequency (RF) impairments,
channel fading, noise and interference, the signal arriving at the receiver
will be distorted. The receiver needs to recover the original information from
the distorted signal. In this paper, we propose a new receiver model, namely
DeepReceiver, that uses a deep neural network to replace the traditional
receiver's entire information recovery process. We design a one-dimensional
convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is
used to improve the adaptability of the network to different input signal
lengths. Multiple binary classifiers are used at the final classification layer
to achieve multi-bit information stream recovery. We also exploit the
DeepReceiver for unified blind reception of multiple modulation and coding
schemes (MCSs) by including signal samples of corresponding MCSs in the
training set. Simulation results show that the proposed DeepReceiver performs
better than traditional step-by-step serial hard decision receiver in terms of
bit error rate under the influence of various factors such as noise, RF
impairments, multipath fading, cochannel interference, dynamic environment, and
unified reception of multiple MCSs.
