Robust Deep Sensing Through Transfer Learning in Cognitive Radio
This work addresses robustness issues in cognitive radio sensing for secondary users, but it appears incremental as it builds on existing deep sensing methods with transfer learning.
The paper tackles the problem of degraded performance in deep learning-based spectrum sensing when applied to different wireless scenarios by incorporating transfer learning, resulting in a robust framework validated through results.
We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing is effective when operating in the same scenario as the collected training data, the sensing performance is degraded when it is applied in a different scenario with different wireless signals and propagation. We incorporate transfer learning into the framework to improve the robustness. Results validate the effectiveness as well as the robustness of the proposed deep spectrum sensing framework.