We present a neural network architecture able to efficiently detect
modulation scheme in a portion of I/Q signals. This network is lighter by up to
two orders of magnitude than other state-of-the-art architectures working on
the same or similar tasks. Moreover, the number of parameters does not depend
on the signal duration, which allows processing stream of data, and results in
a signal-length invariant network. In addition, we have generated a dataset
based on the simulation of impairments that the propagation channel and the
demodulator can bring to recorded I/Q signals: random phase shifts, delays,
roll-off, sampling rates, and frequency offsets. We benefit from this dataset
to train our neural network to be invariant to impairments and quantify its
accuracy at disentangling between modulations under realistic real-life
conditions. Data and code to reproduce the results are made publicly available.

我们提出了一种神经网络架构，能够高效地检测 I/Q 信号中的调制方式，对同类任务来说其计算量比其他最先进的架构轻两个数量级。此外，我们生成了一个基于仿真的数据集，用于训练网络对 I/Q 信号的失真具有不变性，并在现实生活中的实际条件下量化了其精度。