## 8 Conclusions  
The paper deals with the topic of spectrum awareness, specifically the preclassification of detected signals in the wideband radio spectrum. Spectrograms were proposed as a means to apply convolutional neural networks (CNNs) in the discussed context, as the waterfall visualization depicts signal characteristics in the frequency-time-amplitude domain as a sequence of interconnected pixels, forming geometric objects easily interpretable by CNNs.  
The focus of the study was on the preclassification and clustering of signals in the wideband radio spectrum. Preclassification serves the purpose of grouping similar signals and performing an initial clustering of radio signals received by a wideband receiver based on a latent feature vector generated by the network structure. An unsupervised (self-supervised) learning method was proposed, using a contrastive loss function. The approach was inspired by the SimCLR solution, but the network training strategy was modified and adapted to the problem of distinguishing signal modulations for different transform sizes, spectrogram durations, SNR coefficients, etc. An author-created database was used for training and evaluation purposes.  
As part of the evaluation of the trained network model, simple searching for similar signals (based on modulation) in radio spectrograms was presented, along with the capability of automatic grouping and visualization of similar signals in the wideband radio spectrum.  
The paper demonstrated the feasibility of employing deep convolutional neural networks in the analysis of wideband radio spectrum for building artificial intelligence systems operating in the domain of the radio electromagnetic environment. The preclassification of signals, exemplified in the paper using modulation as an example, can be realized for various parameters of radio signals or even radio fingerprints. This opens up new possibilities in autonomous spectral analysis, including spectrum monitoring conducted by civilian authorities or electronic warfare on the battlefield.  
Investigation, A.O. and Z.P.; methodology, A.O. and Z.P.; resources, A.O.; supervision, Z.P.; validation, Z.P.; writing--original draft, A.O. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Visualization of signals similar to signal 4 for \(\lambda=6\) (**a**) and \(\lambda=11\) (**b**).  
**Funding:** This research was funded by the Military University of Technology, Faculty of Electronics, grant number UGB 22 747 on Application of artificial intelligence methods to cognitive spectral analysis, satellite communications and watermarking and technology Deepfake.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The training dataset used to train the neural network was prepared by the authors and publicly shared at: [[https://github.com/aolesinski/rfspec-db](https://github.com/aolesinski/rfspec-db)]([https://github.com/aolesinski/rfspec-db](https://github.com/aolesinski/rfspec-db)) (accessed on 3 May 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.