  Non-cooperative communications, where a receiver can automatically
distinguish and classify transmitted signal formats prior to detection, are
desirable for low-cost and low-latency systems. This work focuses on the deep
learning enabled blind classification of multi-carrier signals covering their
orthogonal and non-orthogonal varieties. We define two signal groups, in which
Type-I includes signals with large feature diversity while Type-II has strong
feature similarity. We evaluate time-domain and frequency-domain convolutional
neural network (CNN) models in simulation with wireless channel/hardware
impairments. Simulation results reveal that the time-domain neural network
training is more efficient than its frequency-domain counterpart in terms of
classification accuracy and computational complexity. In addition, the
time-domain CNN models can classify Type-I signals with high accuracy but
reduced performance in Type-II signals because of their high signal feature
similarity. Experimental systems are designed and tested, using software
defined radio (SDR) devices, operated for different signal formats to form full
wireless communication links with line-of-sight and non-line-of-sight
scenarios. Testing, using four different time-domain CNN models, showed the
pre-trained CNN models to have limited efficiency and utility due to the
mismatch between the analytical/simulation and practical/real-world
environments. Transfer learning, which is an approach to fine-tune learnt
signal features, is applied based on measured over-the-air time-domain signal
samples. Experimental results indicate that transfer learning based CNN can
efficiently distinguish different signal formats in both line-of-sight and
non-line-of-sight scenarios with great accuracy improvement relative to the
non-transfer-learning approaches.
