## 4 Results and Discussion
However, the other two algorithms are experiencing relatively higher packet loss as they do not consider these parameters that cause lower cell-edge throughput. Figures 3-5 also show that EMLWDF outperforms the other algorithms for all types of user data. The proposed EMLWDF is on average 88.59% and 46.04% better than PF and MLWDF algorithms regarding cell-edge throughput for Video type data. Moreover, EMLWDF increases the cell-edge throughput by 72.17% and 39.33% for VoIP and 77.30% and 13.93% for HTTP types of data compared with the PF and MLWDF algorithms, respectively. Table 4 shows the average improvement of proposed EMLWDF for different types of data. Average user throughput is calculated by considering all the users of the cell area to demonstrate that the proposed EMLWDF algorithm is not only efficient for the cell-edge user but also for all other users of the cell. Figures 6-8 show the average user throughput of different algorithms for three different data types. All of the figures show that the EMLWDF algorithm achieves better average user throughput than the other algorithms. The EMLWDF algorithm is more effective for the cell-edge user throughput than the other algorithms. Figure 4: Cell-edge user throughput for VoIP. Figure 5: Cell-edge user throughput for HTTP. throughput than the other two algorithms. Although EMLWDF has marginal improvements for VoIP type data in Figure 6, it still performs 31.80% and 4.86% better than PF and MLWDF, respectively. The reasons for this higher user throughput are (a) the EMLWDF algorithm considers the distance and the received SINR only when the users are located outside the inner region. Otherwise, it distributes the RBs among the users based on HOL delay and throughputs. Therefore, all users get the required RBs and can maintain the throughput. In addition, (b) the EMLWDF algorithm also ensures that other users in the cell center area do not starve for RBs due to prioritizing the cell-edge user by taking the logarithm expression of the distance and received SINR rather than direct implementation. Thus, the cell-edge users will not be allocated to all of the available RBs and the scheduler can serve the cell center users according to their requirements. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic Model** & **Algorithm** & **Cell-Edge User Throughput (\%)** & **Average User Throughput (\%)** \\ \hline \multirow{2}{*}{Video} & PF & 88.59 & 27.23 \\  & MLWDF & 46.04 & 11.57 \\ \hline \multirow{2}{*}{VoIP} & PF & 72.17 & 31.80 \\  & MLWDF & 39.33 & 4.86 \\ \hline \multirow{2}{*}{HTTP} & PF & 77.30 & 44.71 \\  & MLWDF & 13.93 & 19.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Improvement comparison of proposed EMLWDF method. Figure 6: Average user throughput for Video. Figure 7: Average user throughput for VoIP. The fairness index of the user is calculated by the [PERSON]'s fairness index equation [31], which can be described as, \(F=(\sum_{i=1}^{n}\beta_{i})^{2}/n\sum_{i=1}^{n}\beta_{i}^{2}\). Here, \(F\) is the fairness index, \(\beta_{i}\) is the average throughput of user \(i\), and \(n\) denotes the number of users. The fairness index \(F\) ranges from \(1/n\) to 1. Mixed data types for all users in the cell are considered to calculate the fairness. Figure 9 shows the comparison of the fairness index among different algorithms with a various number of users. It is clear from this comparison that EMLWDF performs better than the other algorithms. EMLWDF has 43.77% and 4.66% better fairness than PF and MLWDF, respectively. By considering the distance of the user and received SINR along with HOL delay and the probability of packet loss, the proposed EMLWDF distributes RBs more efficiently to all users based on their requirements. The users with high priority application and higher delay along with cell-edge users get the higher priority in this method to get the RBs, which enhances the throughput as well as fairness index.