Seeds: Sampling-Enhanced Embeddings

A start-stop points CenterNet for wideband signals detection and time-frequency localization in spectrum sensing

Recently, deep learning (DL) based object detection methods have attracted significant attention for wideband multisignal detection, which has been viewed as an essential part in the field of cognitive radio spectrum sensing. However, the existing DL methods are difficult or very likely fail to detect discontinuous burst signals, not to mention the signals with wide, instantaneous, dynamic bandwidth, and multiple channels. To solve this problem, the present study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, namely the Fast Spectrum-Size Self-Training network (FSSNet). Considering the horizontal rectangle form of a wideband signal in the time-frequency domain, we innovatively utilize the start-stop points of the two-dimensional (2D) Box to build the signal model. Specifically, We propose a fast Start-stop HeatMap where the proposed LPS-YXE simultaneously labels and divides the start-stop points positions in the X-Y axis of a single HeatMap. We attribute the method's success in discontinuous signal detection to the multidimensional space transformation of HeatMap, which is used to locate the start-stop points and extract features separated from the signal regions of start-stop points. Furthermore, FSSNet can realize the 2D Box estimation of the wideband signal by regressing only a single variable, and thus with satisfactory detection speed. Simulation results verify the effectiveness and superiority of the proposed start-stop based wideband signal detection scheme with practical received signals. All our models and code are available athttps://github.com/jn-z/SSNet2.