基于自注意力解码的UHF-RFID多标签碰撞恢复
Multi-Tag Collision Recovery in UHF-RFID Using Self-Attention Decoding
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中文总结 AI 辅助
该研究针对UHF-RFID多标签碰撞导致传统协议吞吐量受限的问题，提出SATR算法，可解码最多4个标签的碰撞，吞吐量较传统FSA提升2.2至5.1倍，性能优于现有方法。
中文摘要 AI 辅助
无源超高频（UHF）射频识别（RFID）技术可实现无电池标签通过反向散射与阅读器通信。当多个标签在同一时隙响应时，其波形在阅读器处重叠，遵循帧时隙ALOHA（FSA）协议的传统阅读器会丢弃该碰撞时隙，尽管接收信号仍包含可恢复的响应标签信息，这限制了整体协议的吞吐量。为解决该问题，本文提出自注意力标签恢复（SATR）算法，这是一种基于Transformer的解码算法，直接处理标准标签响应期间接收的基带同相和正交（I/Q）样本。SATR利用自注意力对调制波形的时间结构进行建模，并学习候选标签表示，可联合估计响应标签数量，更重要的是能解码每个检测到的标签的比特序列。本文在不同碰撞规模和恢复配置下，通过数值评估SATR的解码和吞吐量性能，并采用商用UHF-RFID标签的测量值进行验证。结果表明，经合理设计和训练，SATR可可靠解码最多4个标签的碰撞；在单确认场景下，其吞吐量约为0.815个标签/时隙，全恢复场景下约为1.87个标签/时隙，分别对应传统FSA吞吐量极限（1/e≈0.368个标签/时隙）的2.2倍和5.1倍，同时接近最优解码性能，且优于现有碰撞恢复方法。
英文摘要
Passive ultra high frequency (UHF) radio frequency identification (RFID) enables battery-free tags to communicate with a reader through backscatter. When multiple tags respond in the same time slot, their waveforms overlap at the reader, and a conventional reader that follows framed slotted ALOHA (FSA) discards the resulting collided slot. This limits the throughput of the overall protocol even though the received signal still contains recoverable information about the responding tags. To address this limitation, we propose Self-Attention Tag Recovery (SATR), a transformer-based decoding algorithm that operates directly on the baseband in-phase and quadrature (I/Q) samples received during a standard tag response. SATR uses self-attention to model the temporal structure of the modulated waveform and learns candidate tag representations. It jointly estimates the number of responding tags and, more importantly, decodes the bit sequence of each detected tag. We numerically evaluate the decoding and throughput performance of SATR over a range of collision sizes and recovery configurations, and validate it with measurements of commercial UHF-RFID tags. The results show that, with proper design and training, SATR can reliably decode collisions of up to four tags. It achieves a throughput of approximately $0.815$ tags per slot under single acknowledgment and $1.87$ tags per slot under full recovery, corresponding to $2.2$ and $5.1$ times the conventional FSA limit of $1/e \approx 0.368$ tags per slot, while approaching optimal decoding performance and outperforming existing collision recovery methods.
