Multi-Tag Collision Recovery in UHF-RFID Using Self-Attention Decoding

Abstract

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.