Learning modulation filter networks for weak signal detection in noise

Weak signal detection is a challenging yet significant problem in the field of radio communication. Although hand-crafted filters are widely used in signal processing, they are challenged by the weak signal detection task with unknown background noise especially in the range of 0-5dB. In this paper, we propose the learning modulation filter networks (LMFNs) to improve the detection performance. The approach is based on a two-stage optimization scheme which addresses filter learning, attention mechanism and classification in a unified framework. Modulation filters are built to enhance the capacity of the learned filters, and the attention mechanism further characterizes the saliency properties of the input signal. LMFNs reduce the storage size of the network while achieving the state-of-the-art performance by a significant margin compared to traditional cognitive radio approaches. We establish a weak signal dataset that contains unmanned aerial vehicle (UAV) communication signals in a real-terrain environment. The source code and dataset will be made publicly available soon.