# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 4 Results and Discussion
### Simulation Results  
The average throughput of the user is calculated for different methods and data types with a various number of users. The ratio of total number of bits of active user and the total transmission time is considered as average user throughput. The inactive users are not considered in this case. Figures 3-5 show the comparison of proposed, LL-PF, LG-PF, and RR methods for different types of data. The proposed method performs 16.73%, 26.82%, 27.95% better than LG-PF; 28.30%, 35.62%, 39.40% better than LL-PF; and 32.51%, 31.29%, 29.71% better than RR method for video, HTTP, and VoIP, respectively. As predicted, when the number of users increases, the average throughput decreases for all methods. The reasons are as follows: (a) the proposed method assigns CCs to the user by considering channel quality along with the queue length of the CCs which assures higher transmission rate and effective load balancing (however, other methods assign CCs to the user by considering only channel quality of the CCs or arbitrary selection scheme); (b) The proposed method considers the error probability for the user data and maximum delay budget of specific data type, whereas other methods do not consider these parameters, as a result, these methods incurs significant  
Figure 4: Average user throughput for VoIP. Figure 5: Average user throughput for HTTP. Figure 3: Average user throughput for video. Figure 8: Average cell throughput for HTTP. Figure 6: Average cell throughput for video. Figure 7: Average cell throughput for VoIP. The average cell throughput is affected by the following aspects: (a) when there is a lower number of users in the cell, more users will have a higher number of RBs with higher channel quality which leads to an increase in the cell throughput (b) for a higher number of users, fewer users will have RBs with higher channel quality which causes lower cell throughput. For the above reasons, average cell throughputs are highest for 10 users and lowest for 50 users in Figures 6-8. When the number of users increases, average cell throughput decreases as more users share the total cell capacity. The proposed method shows 13.43%, 14.7%, 16.87% better than LG-PF, 19.86%, 10.48%, 31.81% better than LL-PF; and 6.38%, 12.64%, 17.14% better than RR method for video, HTTP, and VoIP, respectively. After analyzing the above results, it is clear that the proposed method ensures the QoS for all three types of data. Unlike the other methods, it prioritizes not only the previous rate or channel condition, but also considers HOL delay, queue condition of the CCs, and probability of packet loss. Thus, the average cell throughputs for all types of data are similar in which it proves that the proposed method can provide better QoS to all users in the cell area under all scenarios. [PERSON]'s fairness index is used to calculate and compare the fairness of the user [35]. Figure 9 presents the fairness index of different methods with various numbers of users. It is clear from this figure that the proposed method is better for fewer users in the cell and the performance is very close to each other when the number of the users increases. By considering the HOL delay and probability of packet loss, the proposed method distributes RBs efficiently to all users depending on their situation. Cell-edge users and users with bad channel quality have required RBs, which increases the fairness index. However, LG-PF, LL-PF, and RR methods assign RBs to the users without considering delay and probability of packet loss which leads to lower index. The fairness index decreases for higher number of users due to the limitation of the resources in the cell. Despite all constraints, on average, the proposed method is 10.48%, 11.49%, and 5.56% better than LG-PF, LL-PF, and RR, respectively.