Dynamic Frame Slotted ALOHA Algorithm Based on BP Neural Network

In recent years, Radio Frequency Identification (RFID) technology is more widely utilized around varieties of industries. When dealing with a large amount of labels, RFID system often encounters serious collisions due to the label response, resulting in the problem of low reading efficiency. The key to solve this problem is the speed and accuracy of label number estimation algorithm. Based on the analysis of the traditional algorithm, a new label quantity estimation algorithm is proposed, which applies the BP neural network to establish the mapping relationship between the last frame read and the number of remaining labels in the reader. The simulation results prove that the proposed algorithm can effectively reduce the time consumption and improve the system efficiency compared with the traditional algorithms.