$Q$ -Value Prediction Dynamic Framed-Slotted Aloha Algorithm
Shi Guan, Gan Luan · 2024
To enhance the efficiency of radio frequency identification and reduce system computational complexity, this paper proposes a$Q$- value prediction dynamic framed-slotted Aloha algorithm. The advantage of this paper is that the$Q$- value is predicted by the binary tree splitting principle, so that the number of tags can be accurately and quickly predicted, and the initial frame length that best matches the number of tags can be provided for the system, thus achieving stable and efficient system identification efficiency and time efficiency.Additionally, simulation results indicate that for any number of tags, the system efficiency of this algorithm exceeds 0.32, and the time efficiency surpasses 0.72, thereby demonstrating superiority over existing algorithms.