We propose a collision recovery algorithm with the aid of machine learning (ML-aided) for passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) systems. The proposed method aims at recovering the tags under collision to improve the system performance. We first estimate the number of tags from the collided signal by utilizing machine learning tools and show that the number of colliding tags can be estimated with high accuracy. Second, we employ a simple yet effective deep learning model to find the experienced channel coefficients. The proposed method allows the reader to separate each tag's signal from the received one by applying maximum likelihood decoding. We perform simulations to illustrate that the use of deep learning is highly beneficial and demonstrate that the proposed approach boosts the throughput performance of the standard framed slotted ALOHA (FSA) protocol from 0.368 to 1.756, where the receiver is equipped with a single antenna and capable of decoding up to 4 tags.
翻译:我们建议对被动超高频无线电频率识别系统采用碰撞回收算法,借助机器学习系统(ML-辅助)进行被动超高频无线电频率识别(RFID)系统。拟议方法旨在恢复碰撞中的标记以改善系统性能。我们首先利用机器学习工具估计相撞信号中的标记数量,并显示对相撞标记的数量可以非常精确地估计。第二,我们使用简单而有效的深层学习模型寻找有经验的频道系数。拟议方法允许读者通过应用最大可能的解码将每个标记的信号与收到标记的信号分开。我们进行模拟,以说明深层学习的使用非常有益,并表明拟议方法提高了标准方形ALOHA(FA)定槽协议的吞吐量性能,从0.368到1.756,接收器配备了单一的天线,能够解码到4个标记。