A light neural network for modulation detection under impairments
This work addresses modulation detection for wireless communication systems under impairments, but it is incremental as it focuses on efficiency and invariance improvements.
The authors tackled the problem of detecting modulation schemes in I/Q signals under impairments by proposing a lightweight neural network that is up to 100 times smaller than state-of-the-art methods and invariant to signal length, achieving accuracy in realistic conditions.
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.