## 2 Related Works
### Distance Prediction  
In [19], the authors proposed a deep learning approach for user equipment positioning in non-line-of-sight scenarios. The impact of variables such as the type of radio data, the number of base stations, the size of the training dataset, and the generalization of the trained models on third-generation partnership project (3 GPP) indoor factory scenarios was analyzed. The model trained consisted of a customized residual neural network (ResNet) for the path gain dataset and ResNet-18 for the channel impulse response dataset. The metric used was the 90% quantile of the cumulative distribution function of the horizontal positioning error. The authors obtained the best performance with a large number of samples for training and tuning the models.  
In [20], the authors proposed a machine learning algorithm for indoor fingerprint positioning based on measured 5G signals. The dataset was created by collecting 5G signals in the positioning area and processing them to form fingerprint data. A CNN was trained to locate a 5G device in an indoor environment. The metrics used were the root mean square error and the circular error probable. The experiments were conducted in a real field and demonstrated a positioning accuracy of 96% for the proposed method.  
Finally, in [21], the authors introduced a location-aware predictive beamforming approach utilizing deep learning techniques for tracking unmanned aerial vehicle communication beams in dynamic scenarios. Specifically, they designed a recurrent neural network called a location-aware recurrent network (LRNet), based on LSTM, to accurately predict unmanned aerial vehicle locations. Using the predicted location, it was possible to determine the angle between the unmanned aerial vehicle and the base station for efficient and rapid beam alignment in the subsequent time slot. This ensures reliable communication between the unmanned aerial vehicle and the base station. The simulation results demonstrate that the proposed approach achieves a highly satisfactory unmanned aerial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Research Direction** & **Contributions** & **Limitations** \\ \hline \multirow{4}{*}{[17]} & SNR estimation & Proposed a DINet for & \\  & method based on SRS & SNR estimation, which & The number of testing \\  & and a deep & is a DnCNN and & samples is small \\  & learning network & IRCNN in parallel & \\ \hline \multirow{4}{*}{[11]} & Method for estimating & They used a & \\  & SNR in LTE systems & CNN-LSTM neural & The proposed method \\  & and 5G & network to extract & exhibited lower \\  & and 5G & spatial and & performance in the \\  & & temporal features & frequency domain \\ \hline \multirow{4}{*}{[18]} & Novel neural network & Proposed an NDR-Net & \\  & for channel estimation & for channel estimation, & \\  & in the presence of & which comprises an & \\  & unknown noise levels & NLES, a DnCNN, and & \\  & & an RLC & \\ \hline \multirow{4}{*}{Proposed} & Prediction of noise & Proposed several & \\  & power density in & & \\  & spectrum & & \\  & sensing signals & several other variables & \\  & & that influence the & \\  & & quality of the signal & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related works on noise prediction.  
vehicle to base station communication rate, approaching the upper limit attained by a perfect genie-aided alignment scheme.  
In Table 2, some techniques for distance prediction are summarized and compared. The methodology proposed in this article considers several variables that influence signal quality for data generation. The study incorporates Euclidean distances between users across a wide range of values and explores a spectrum sensing environment where multiple users are in motion at fixed speeds over time. Several regression models are compared using various metrics to highlight the robustness of the proposed method.