## 3 Spectrum Management for CR-VANET Using MCDM
### Channel Decision Based on Channel Ranking
#### 3.2.2 Qualitative Analysis for AHP
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & \(\text{r}_{\text{max}}>\text{BW}>\text{S}_{\text{noise}}>\text{CDF}>\text{PLR}> \text{P}_{\text{sig}}\) \\ \hline
**Text** & \(\text{PLR}>\text{S}_{\text{noise}}>\text{P}_{\text{sig}}>\text{CDF}>\text{BW}> \text{r}_{\text{max}}\) \\ \hline
**Voice** & \(\text{S}_{\text{noise}}>\text{P}_{\text{sig}}>\text{BW}>\text{PLR}>\text{CDF}> \text{r}_{\text{max}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for ordering preference for decision making. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CDF** & **BW** & **PLR** & \(\text{P}_{\text{sig}}\) & \(\text{S}_{\text{noise}}\) & \(\text{r}_{\text{max}}\) \\ \hline CDF & 5/5 & 5/3 & 5/9 & 5/6 & 5/7 & 5/2 \\ BW & 3/5 & 3/3 & 3/9 & 3/6 & 3/7 & 3/2 \\ PLR & 9/5 & 9/3 & 9/9 & 9/6 & 9/7 & 9/2 \\ \(\text{P}_{\text{sig}}\) & 6/5 & 6/3 & 6/9 & 6/6 & 6/7 & 6/2 \\ \(\text{S}_{\text{noise}}\) & 7/5 & 7/3 & 7/9 & 7/6 & 7/7 & 7/2 \\ \(\text{r}_{\text{max}}\) & 2/5 & 2/3 & 2/9 & 2/6 & 2/7 & 2/2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative rating Matrix “Z”. The sum of row \(SumR(i)\) is normalized to calculate the priority of ordering mentioned in Equation (18). \[Normalized\_SumR(i)=SumR(i)/total\ (SumR(i)),\ i=1,2,\ldots,\text{m} \tag{18}\]

Therefore:

\[Total\ (rSum(i))=\sum_{i=1}^{m}rSum(i) \tag{19}\]

Finally, by considering the various factors, the performance is analyzed by calculating the weight. The consistency ratio is calculated by dividing an index of consistency (\(CI\)) and random (\(RI\)) values, and it is mentioned in Equation (20). In addition, \(n\) is the number of DAHP factors. Calculated weighs for performance factors are presented in Table 6.

\[Crat=\frac{CI}{RI}, \tag{20}\]

\[CI=\frac{\lambda_{max}-n}{RI}, \tag{21}\]

Therefore, \(n\) and \(R\) values of \(RI\) are \(n=1\), \(2\), \(\ldots,8\), with respective \(0.0\), \(0.0\). \(0.5\), \(0.9\), \(1.1\), \(1.2\), \(1.3\), \(1.4\). \(\lambda_{max}\) can be calculated as average(rSum) of weighted comparative matrix. By combining Equations (20) and (21) various data services, such as data, audio, and video, can be provided by calculating CRAT values, and they are less than \(0.1\) in value, as mentioned in Table 7.

Computation of the normalized sum of row is calculated in Equation (22). \[\sum_{i=1}^{m}Normalized\_rSum(i)=1 \tag{22}\]

The sum of normalized row \(SumR(i)\) helps to reduce the weight of each channel by DAHP qualitative analysis metrics by calculating the parameters of text, audio, and video, and it is mentioned in Table 8. \begin{table}
\begin{tabular}{c c} Channel Deciding Factor (\%) & \(0.15625\) \\ Bandwidth in MHz & \(0.09375\) \\ Packet loss ratio in \% & \(0.28125\) \\ Signal power in dBm & \(0.1875\) \\ Signal-to-noise ratio in dB & \(0.21875\) \\ Maximum rate in bits per sec & \(0.0625\) \\ \end{tabular}
\end{table} 
Table 6: Calculated weights for performance factors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Service** & **Text** & **Audio** & **Video** \\ \hline
**Consistency Index (CD)** & \((0.00833333)\) & \(-1.15944\) & \(0.0083333333\) \\
**CRAT** & \((0.006944444)\) & \(-0.9662\) & \(0.006944444\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CRAT values calculated for sample criteria. \begin{table}
\begin{tabular}{c c} \hline \hline
**Service** & **Text** & **Audio** & **Video** \\ \hline
**Consistency Index (CD)** & \((0.00833333)\) & \(-1.15944\) & \(0.0083333333\) \\
**CRAT** & \((0.00694444)\) & \(-0.9662\) & \(0.00694444\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CRAT values calculated for sample criteria. By comparing all qualitative parameters, we can see in Table 8 that packet loss ratio is comparatively less in text data when compared with audio and video data. Therefore, channel quality is compared by transmitting all kinds of data formats, which are mentioned in Figure 4. The signal-to-noise ratio is the maximum weight of CAV. Therefore, CAV helps to decide the channel quality for all data services.