  Identification of the type of communication technology and/or modulation
scheme based on detected radio signal are challenging problems encountered in a
variety of applications including spectrum allocation and radio interference
mitigation. They are rendered difficult due to a growing number of emitter
types and varied effects of real-world channels upon the radio signal. Existing
spectrum monitoring techniques are capable of acquiring massive amounts of
radio and real-time spectrum data using compact sensors deployed in a variety
of settings. However, state-of-the-art methods that use such data to classify
emitter types and detect communication schemes struggle to achieve required
levels of accuracy at a computational efficiency that would allow their
implementation on low-cost computational platforms. In this paper, we present a
learning framework based on an LSTM denoising auto-encoder designed to
automatically extract stable and robust features from noisy radio signals, and
infer modulation or technology type using the learned features. The algorithm
utilizes a compact neural network architecture readily implemented on a
low-cost computational platform while exceeding state-of-the-art accuracy.
Results on realistic synthetic as well as over-the-air radio data demonstrate
that the proposed framework reliably and efficiently classifies received radio
signals, often demonstrating superior performance compared to state-of-the-art
methods.
