## 3 Methods
### System Model  
The use of generated data simulating PU signals is proposed in spectrum sensing to predict the noise level of the sensed signal and the initial and final distances between users. For this, the methodology is divided as follows: (1) database generation, where the signals that represent PUs are generated [1,2]; (2) training the proposed regression models [22,23]; and (3) evaluation of the trained models for predicting noise level and distance [13,24]. At the end of the process, it is expected that the models will be able to predict the level of noise and the initial and final distances between the PU and SU during the sensing period. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Research Direction** & **Contributions** & **Limitations** \\ \hline \multirow{4}{*}{[19]} & User equipment positioning in & Proposed customized ResNet for & Without finetuning and with \\  & non-line-of-sight scenarios & the path gain dataset and & large number of samples, the \\  & non-line-of-sight scenarios & ResNet-18 for the channel & models exhibited \\  & & impulse response dataset & lower performance \\ \hline \multirow{4}{*}{[20]} & & A CNN was trained to locate a & \\  & & 5G device in an indoor & \\  & Indoor fingerprint positioning & environment. The experiments & The proposed method is not \\  & based on measured 5G signals & were conducted in a real field & compared with other deep \\  & & and demonstrated a positioning & learning models \\  & & accuracy of 96\% for the & \\  & & proposed method & \\ \hline \multirow{4}{*}{[21]} & & Designed a recurrent neural & \\  & & network called LRNet, based on & \\  & Location-aware predictive & LSTM, to accurately predict & \\  & beamforming approach utilizing & unmanned aerial vehicle & \\  & deep learning techniques for & locations. Using the predicted & \\  & tracking unmanned aerial & location, it was possible to & \\  & vehicle communication beams in & determine the angle between the & \\  & dynamic scenarios & unmanned aerial vehicle and the & \\  & & base station for efficient and & \\  & & rapid beam alignment in the & \\  & & subsequent time slot & \\ \hline \multirow{4}{*}{Proposed} & & Proposed several regression & \\  & Prediction of initial and final & algorithms for predicting & \\  & distances between users during & distances between users & \\  & spectrum sensing & considering several other & \\  & & variables that influence the & \\  & & quality of the signal & \\ \hline \end{tabular}
\end{table} 
Table 2: Related works on distance prediction. The dataset utilized for training the proposed regression models comprises signal data, noise levels, and distances between the users. Initially, the SU and PU are positioned within the area \(X\times Y\), moving randomly for a duration of \(\Delta t\). During this time, signals are sensed, and data on noise levels (\(N_{0}\)) as well as initial (\(D_{i}\)) and final (\(D_{f}\)) distances are collected. These collected data are then used to train and test the proposed regression models. To validate the effectiveness of the method in predicting noise levels and distances, various metrics are calculated. Figure 1 illustrates the representation of the environment just described. The initial state (left) is an example of how users can be positioned within the area, while the final state (right) demonstrates the change in position after \(\Delta t\). The PU, marked by a red diamond, and the SUs, represented by black triangles, are randomly positioned within the area. In the initial state, the heatmap represents the distance of each SU from the PU, with warmer (red) areas indicating closer proximity and cooler (blue) areas showing greater distances. In the final state, we can observe that the PU and SUs have changed positions, and the distances between the SUs may also vary during the observation period. The output of step (1) is expected to be the signals that represent the PU, the associated noise level for each signal, and the initial and final distances during the sensing.