MagMap: A Parallel Decoding Scheme for Weak RFID Signals Using Middle State Points and Magnitude Extraction

As RFID systems become increasingly widespread, the limitations imposed by tag collisions on system performance are becoming more evident. Parallel decoding has attracted significant attention due to its ability to improve channel utilization and throughput. However, existing schemes often perform poorly when decoding weak signals. Several challenges remain, including the assumption of ideal channel conditions, difficulty in detecting tag state transitions, and the complexity of state cluster formations in the In-phase and Quadrature (IQ) domain. To address the above issues, this paper first experimentally verifies the ability of middle state points to segment tag states, and proposes a time-window-based pre-processing method to improve the density of state clusters in the IQ domain. Second, by leveraging the high vertical resolution of the reader, we propose an ideal magnitude calculation method and a matching strategy for combined state clusters under weak signal conditions. Finally, we propose MagMap, a parallel decoding scheme based on middle state points and magnitude extraction. Experimental results demonstrate that, under weak signal conditions, MagMap reduces the decoding BER (Bit Error Ratio) of received packets by more than 60% compared to the state-of-the-art.