ML辅助的UHF-RFID系统冲突恢复
ML-Aided Collision Recovery for UHF-RFID Systems
摘要：
提出了一种基于机器学习（ML辅助）的无源超高频（UHF）射频识别（RFID）系统碰撞恢复算法。该方法的目的是恢复碰撞下的标签，以提高系统的性能。我们首先利用机器学习工具估计碰撞信号中的标签数量，并表明可以高精度地估计碰撞标签的数量。其次，我们采用简单而有效的深度学习模型来找到经验信道系数。所提出的方法允许读取器通过应用最大似然解码将每个标签的信号与接收到的信号分离。我们进行模拟来说明使用深度学习是非常有益的，并证明所提出的方法将标准帧时隙ALOHA（FSA）协议的吞吐量性能从0.368提高到1.837，其中接收器配备有单个天线并且能够解码多达4个标签。
英文摘要：
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 1.837, where the receiver is equipped with a single antenna and capable of decoding up to 4 tags.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
