  Recently, deep learning (DL) has been emerging as a promising approach for
channel estimation and signal detection in wireless communications. The
majority of the existing studies investigating the use of DL techniques in this
domain focus on analysing channel impulse responses that are generated from
only one channel distribution such as additive white Gaussian channel noise and
Rayleigh channels. In practice, to cope with the dynamic nature of the wireless
channel, DL methods must be re-trained on newly non-aged collected data which
is costly, inefficient, and impractical. To tackle this challenge, this paper
proposes a novel universal deep neural network (Uni-DNN) that can achieve high
detection performance in various wireless environments without retraining the
model. In particular, our proposed Uni-DNN model consists of a wireless channel
classifier and a signal detector which are constructed by using DNNs. The
wireless channel classifier enables the signal detector to generalise and
perform optimally for multiple wireless channel distributions. In addition, to
further improve the signal detection performance of the proposed model,
convolutional neural network is employed. Extensive simulations using the
orthogonal frequency division multiplexing scheme demonstrate that the bit
error rate performance of our proposed solution can outperform conventional
DL-based approaches as well as least square and minimum mean square error
channel estimators in practical low pilot density scenarios.
