结合BP神经网络的动态帧时隙ALOHA改进算法

Improved Algorithm of Dynamic Frame Slot ALOHA Based on BP Neural Network

近年来,随着射频识别技术在生产生活中的广泛应用,人们对于标签读取系统的要求也越来越高.当面对大规模的标签数量时,射频识别系统常常因为标签响应在同一读取帧中发生严重的碰撞,导致出现读取效率降低的问题,而解决该问题最关键的是标签数量的估计算法是否快速准确.文章在分析传统算法的基础上,提出了一种新的标签数量估计算法,基于动态帧时隙算法的读取原则,生成特定帧长下标签数量数据集,然后搭建特定结构的BP神经网络,建立阅读器上一帧读取情况与剩余标签数量的映射关系,实现对标签数量的估计.通过仿真实验结果证明,相比于传统的标签数量估计算法,文章提出的算法在不损失准确度的同时,能有效降低阅读器读取时耗,提升系统效率.

In recent years,with the wide application of RFID technology in production and life,people have more and more high requirements on the tag reading system.When faced with a large number of tags Jthe RFID system often suffers from a serious collision in the same reading frame due to the tag response,which leads to a problem of low reading efficiency.The key to solve this problem is whether the tag number estimation algorithm is fast and accurate.Based on the analysis on the basis of the traditional algorithm,this paper proposes a new tag number estimation algorithm,based on the principle of dynamic frame timeslot algorithm reads generate specific word length Jthe tag number of data sets and then set up a specific structure of the BP neural network,set up a frame on the reader reads the mapping relationship with the rest of the tag number,realize the estimate of the number of labels.The simulation results show that,compared with the traditional tag number estimation algorithm,the proposed algorithm can effectively reduce the reading time consumption of the reader and improve the system efficiency without losing the accuracy.