# Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks
## 5 Conclusions  
In cognitive radio, secondary users have to sense and select the best frequency channel that meets their requirements regarding the quality of service and security before transmitting data. Several channel quality ranking mechanisms have been proposed for helping secondary users in their choice. Nevertheless, most of these ranking mechanisms include the occupancy as a parameter and exclude other key parameters such as channel capacity. In addition, most of these existing ranking techniques consider parameters separately in ranking. In this paper, we have proposed an aggregate utility function of several parameters. The weight associated with each parameter is determined using the nonlinear regression. Our findings demonstrate that the proposed model produces good results and can help secondary users in selecting the best channel for communication. To further improve our research, we plan to investigate the validity of our model with other important parameters, especially those related to security.  
The authors acknowledge the support of the US National Science Foundation NSF under grant #1443861. Y.A., Z.E.M. and N.K. conceived the model, designed the experiments, performed the experiments, analyzed the data, and wrote the paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Channel Ranking**} & \multicolumn{1}{c}{**Concept**} & \multicolumn{1}{c}{**Advantages**} & \multicolumn{1}{c}{**Disadvantages**} \\ \hline
[18; 29; 30] & \(\bullet\) & Rank channels based on availability of the channel & \(\bullet\) & Selection of the channel with the highest holding time & \(\bullet\) & No estimation of the quality of the channels \\ \hline
[21; 22; 23; 24; 25; 26] & \(\bullet\) & Rank channels based on prediction of their state & \(\bullet\) & Selection of the channel with the highest holding time & \(\bullet\) & No estimation of the quality of the channels \\ \hline
[27; 28; 29; 30; 31] & \(\bullet\) & Rank channels based on their availability & \(\bullet\) & A few parameters have been used to determine the transmission parameters & \(\bullet\) & The quality of the channels is not involved in channel selection \\ \hline
[27; 28; 29; 30; 31] & \(\bullet\) & Rank channels based on the capacity & \(\bullet\) & Involves several parameters jointly in the process of the channel ranking & \(\bullet\) & The algorithm does not involve security parameters. \\ \hline \multirow{4}{*}{The proposed model} & \(\bullet\) & Rank channels based on the capacity & \(\bullet\) & Involves several parameters jointly in the process of the channel ranking & \(\bullet\) & The algorithm does not involve security parameters. \\  & \(\bullet\) & A machine-learning model is used to predict the utility & \(\bullet\) & Selection of the channel with lowest occupancy and the highest capacity, allowing high data rate and high holding time & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of channel-ranking algorithms in cognitive radio networks.