  Dynamic spectrum access (DSA) benefits from detection and classification of
interference sources including in-network users, out-network users, and jammers
that may all coexist in a wireless network. We present a deep learning based
signal (modulation) classification solution in a realistic wireless network
setting, where 1) signal types may change over time; 2) some signal types may
be unknown for which there is no training data; 3) signals may be spoofed such
as the smart jammers replaying other signal types; and 4) different signal
types may be superimposed due to the interference from concurrent
transmissions. For case 1, we apply continual learning and train a
Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC)
based loss. For case 2, we detect unknown signals via outlier detection applied
to the outputs of convolutional layers using Minimum Covariance Determinant
(MCD) and k-means clustering methods. For case 3, we extend the CNN structure
to capture phase shifts due to radio hardware effects to identify the spoofing
signal sources. For case 4, we apply blind source separation using Independent
Component Analysis (ICA) to separate interfering signals. We utilize the signal
classification results in a distributed scheduling protocol, where in-network
(secondary) users employ signal classification scores to make channel access
decisions and share the spectrum with each other while avoiding interference
with out-network (primary) users and jammers. Compared with benchmark
TDMA-based schemes, we show that distributed scheduling constructed upon signal
classification results provides major improvements to in-network user
throughput and out-network user success ratio.
