Improving Efficiency of Ternary Tree to Support Different Quality of Service Levels
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Abstract
This paper presents three algorithms that improve the performance of Ternary tree to support different quality of service levels. These algorithms are Partial Access type 1, Partial Access type 2 and Adaptive Probability algorithms. For the proposed algorithms, users are divided into two classes, namely class 1 and class 2, with class 1 users given higher priority than class 2 users. In Partial Access type 1 algorithm, class 1 users randomly select one slot from the first two slots, while class 2 users can access all three slots. In Partial Access type 2 algorithm, class 1 users randomly select 1 slot from the first 2 slots, while class 2 users randomly select 1 slot from the last 2 slots. Third algorithm is Adaptive Probability algorithm. In Ternary tree algorithm, each user randomly selects 1 slot out of 3 slots. When viewed in terms of probability, each user randomly accesses each slot with a probability of 1/3. Adaptive probability algorithms use different probability values for each slot. For example, let the probability of accessing slots 1, 2, and 3 be 1/2, 2/5, and 1/10, respectively. Due to the different channel access behavior between class 1 and class 2 users, each class of users has different delay values. Therefore, these three algorithms can be used to support systems that require different quality of service levels. The results show that each algorithm can provide different quality of service, especially Adaptive Probability algorithm, which can adjust its parameters to accommodate different quality of service levels while maintaining an appropriate delay.
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