## 3 Simulation Results
### Discussion  
In this work, five classes are considered under the proposed scheme. No transmission occurred for C0, which can occur due to a higher level of received signal energy or due to low residual energy in the SU node (refer to Table 1). Increasing or decreasing the total number of classes with increases or decreases in the number of reliability thresholds for the received signal energy and/or the number of quantized levels of residual energy in the node will affect the performance of the classifier remarkably, and hence, the achievable throughput. Increases in the number of output classes, to some extent, may improve the performance of the system with a cost of high computation, because increasing the number of classes will increase the number of classifiers used in the _one-versus-rest_ approach. However, improvement in performance is not guaranteed, because an increase in the number of classes also increases the complexity of the classifier. Furthermore, a delay in the classification of classifier will increase. Hence, a trade-off occurs between achievable throughput and computational costs or between throughput and delay.  
Moreover, the machine learning-based classifiers perform better than statistical spectrum sensing techniques such as matched filtering, energy detector, spectral correlation, etc., with a higher computational cost. More energy and resources are used to learn and train the classifier. The sensor nodes or SUs are equipped with batteries of limited capacity. Therefore, the training phase of classifier is performed by the fusion center (FC) and the optimized parameters of trained classifiers are provided to the SU nodes. These SU nodes utilize given parameters for classification of the sensed signal. If any maintenance or update of the training or learning is needed, the FC will do the job and then provide the updated parameters to SU nodes. In this way, the workload of the SU nodes is reduced to conserve energy.  
Furthermore, the classification performance of a system using ML-based classifier is affected significantly by the number and combination of features extracted from the input signal. For a good performance, the features should be used such that the properties of signals from different classes are reflected. Nevertheless, the length of input vector should be as minimal as possible to reduce the computational cost of the system. Therefore, using the lowest possible number of good features may improve the classification performance of the classifier. In this work, the proposed combination of three features improves the classification accuracy of SVM-based classifier for multi-hypothesis spectrum sensing in cognitive radio networks. This claim is verified by the simulation results. Obviously, the performance of the classifier may improve further by using better features combination. However, it is needed to explore more to obtain such a combination of features. A feature selection scheme can be utilized to do the job of selecting the best combination of features from a feature pool with a cost of computational complexity. However, it is out of scope of this paper and can be reserved as a further work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference Energy Packets** & & **20** & **25** & **30** & **35** \\ \hline \multirow{4}{*}{Proposed Scheme} & C\({}_{4}\) & 20 & 25 & 30 & 35 \\  & C\({}_{3}\) & 15 & 20 & 25 & 30 \\  & C\({}_{2}\) & 10 & 15 & 20 & 25 \\  & C\({}_{1}\) & 5 & 10 & 15 & 20 \\ \multirow{2}{*}{_Max Tx Power_} & & 20 & 25 & 30 & 35 \\  & & 5 & 10 & 15 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The relation of reference energy consumption to energy consumed in each transmission scenario.