WCL-SFR: Window-Based Contrastive Learning for Signal Feature Reconstruction

Given the rapid advancement of the physical industrial internet and the growing significance of military reconnaissance, a large amount of signal data will be generated, because signal transmission and reception are the basis of these scenarios. However, due to problems such as air noise, inconsistent transceiver technology, and hacker interference, a large amount of data labels will be lost. With the aim of solving this problem, we propose a Window-based Contrastive Learning for Signal Feature Reconstruction (WCL_SFR) method. This method divides the feature map into small windows and uses similarity to establish a contrastive learning mode, while incorporating a reconstruction module to improve the stability of the model’s capacity for extraction. On two commonly used signal datasets, WCL_SFR generates the most beneficial results, with an improvement of up to 28.32% compared to other contrastive learning methods. In order to simulate real scenarios, we also conducted cross-dataset migration experiments, which achieved accuracies of 65.36% and 66.17%, respectively, which greatly outperformed similar methods. Therefore, WCL_SFR is an innovative development in the field of unsupervised signal recognition.