Blocking rate based call admission control for call dropping mitigation and QoS based prioritization

Author(s):  
Sahawat Namwanta ◽  
Nararat Ruangchaijatupon
Author(s):  
Aminu Mohammed ◽  
Yese Orduen Solomon ◽  
Ibrahim Saidu

Call admission control (CAC) is one of the techniques deployed in managing network resources. Recently, the dynamic QoSaware CAC (DQA-CAC) algorithm was proposed to reduce the new connection blocking rate and improve resource utilization. However, the algorithm admits new connections and degrades existing ones without recourse to delay requirements of delay-intolerant extended real-time polling service (ertPS) and real-time polling service (rtPS) connections—which may incur additional delay, leading to an increase in packet drop rate and, consequently, reduced throughput. In this article, a QoS-guaranteed call admission control (QOG-CAC) algorithm is proposed to ensure QoS and consequently increase the throughput of all class of connections. The proposed QOG-CAC is simulated using a Java-based discrete event simulator. The results of the extensive simulation experiments conducted show that the proposed algorithm outperforms the compared scheme with regard to average system throughput, new connection blocking rate, and per-flow throughput of real-time as well as non-real-time connections


As the demand of the mobile users are increasing day by day, wireless/mobile multimedia networks still need advancement in terms of, reliable traffic performance, link availability, efficient bandwidth utilization, and user mobility, that can attain extremely consistent wireless communication and data transmission over the networks. Due to the emerging demand of multimedia services a high-speed network and call admission control (CAC) scheme is required, which not only guarantees the quality of services (QoS) for new and handoff calls but also results in optimum resource utilization in bursty traffic network environments. The main objective of this integrated neural fuzzy based CAC scheme is to improve QoS with decent resource allocation, such that it minimizes the probability of call dropping and call blocking in mobile multimedia networks. The proposed neural fuzzy CAC scheme is a hybrid approach that integrates the semantic rule ability of fuzzy logic (FL) controller and self-training capability of a neural network (NN) which is further enhanced to construct an efficient computational model for traffic control and fair radio resources allocation for new calls and handoff calls. The simulation results conclude that a neural fuzzy based CAC can achieve minimal call dropping probabilities and maximum resource utilization in high-speed networks as compared to fuzzy logic based CAC and conventional CAC or existing CAC schemes


Author(s):  
Solomon Orduen Yese ◽  
Abdulhakeem Abdulazeez ◽  
Aminu Mohammed

Call admission control (CAC) is a technique deployed in the management of network resources in mobile broadband networks. Despite its importance, the WiMAX technology like most mobile broadband networks does not make provision for CAC, making it an open area of research. A dynamic QoS-Aware Call Admission Control (DQACAC) scheme was recently proposed to improve resource utilization and ensure QoS. Several simulation experiments were conducted to evaluate the performance of the DQA-CAC against other CAC algorithms. The results of the simulation indicated that the DQA-CAC outperformed the existing CAC schemes in terms of reduced new connection blocking rate.  However, the evaluation failed to consider other performance metrics like resource utilization and throughput that are also very crucial in the real-world scenario. This paper therefore attempts to improve the performance of the DQA-CAC by investigating its performance in terms of additional metrics like the average system throughput and resource utilization that further mimic the real-world experience. A Java based discrete event simulator designed for the study was used and the simulation results indicate that the DQA-CAC algorithm outperforms the existing CAC algorithms in terms of improved per-flow throughput, average system throughput and resource utilization in addition to reducing the connection blocking rate.


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