# Signals Intelligence System with Software-Defined Radio
## 5 Conclusions  
This new type of approach that uses cloud computing instead of traditional hardware (CPU or GPU) for artificial intelligence network training eliminates the limitations imposed by dedicated hardware devices. Having the system integrated into the cloud allows quick connection from all around. The user needs only a good internet connection to access and use the system.  
This way of developing and integrating neural networks looks very promising and opens the door to all those who what to study the field of AI and signal processing. Although there are still many things to learn, we have shown that this is possible, and it can be achieved by working hard and dedicating enough time.  
The system presented in this paper was envisioned, developed, and tested over the course of a year. The state-of-the-art phase lasted around three months. The artificial network model implementation and training were the most challenging. It took around six months to design and produce a model that fulfilled all the needs. The hardest part was choosing the cloud platform used and the artificial intelligence network model that would meet the system's specifications. The system integration and testing were the last parts of the paper and lasted around three months.  
To improve the current implementation, the authors intend to extend the modulation database with more data for each modulation in order to have a larger amount of data used to train the model. Additionally, more modulations will be added in order to obtain a model that can be used for a wider range of applications. To achieve the best possible model, multiple configurations of hardware and training parameters will be used. It was noticed that the accuracy decreases with the SNR. To overcome this problem, two possible methods will be used. The first one will consist of the implementation of extra processing  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cloud-Based Neural Networks** & **Local Neural Networks** \\ \hline Less expensive & More flexibility in training and integration \\ \hline The used hardware can be selected based on & When new hardware is needed, it must be \\ user needs & bought \\ \hline Less knowledges required & The average accuracy of all modulations is \\ \hline Easy to configure and train & More training characteristics can be extracted \\  & to check overall performance \\ \hline The trained model can be easily integrated in \\ cloud and accessed from anywhere \\ \hline Comparable accuracy for modulations such as \\ BPSK, FM, and OOK \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cloud-based neural networks vs. local neural networks.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**NN Model** & **16 PSK** & **32 PSK** & **4 ASK** & **8 ASK** & **8 PSK** & **BPSK** & **FM** & **GMSK** & **OOK** & **QPSK** \\ \hline
**This Paper with SNR = 2-30 dB** & 26\% & 27\% & 72\% & 68\% & 25\% & 96\% & 97\% & 88\% & 95\% & 34\% \\ \hline
**Paper [12] with SNR = 2-30 dB** & -75\% & -78\% & -88\% & -90\% & -92\% & -95\% & -95\% & -95\% & -95\% & -95\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of cloud-based neural networks and local neural networks results.  
steps used to improve the signal samples before they are given as input to the neural network. The second method will consist of another neural network model that will aim to reduce noise and improve signal quality. The selection between these two methods will be made after further analysis of both is performed.  
Conceptualization, F.R., T.C.B. and V.P.; methodology, F.R. and M.A.; software, F.R. and T.C.B.; validation, T.C.B. and V.P.; formal analysis, P.A.C. and D.T.C.; writing--original draft preparation, F.R.; writing--review and editing, M.A. and P.A.C.; visualization, D.T.C.; supervision, T.C.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.