Fast monte-carlo clustering for signal separation of RFID collision tags at physical layer

In ultra-high frequency (UHF) radio frequency identification (RFID) systems, enhancing communication efficiency is crucial. This paper proposes a fast Monte-Carlo method to improve the clustering speed of collision signals in a combined ALOHA and physical-layer tag identification technique. The method maps collision signal samples to a 1D axis with a grid, through principal- component-analysis (PCA), determining cluster centers by counting samples in each grid. This approach significantly reduces computational complexity compared to traditional unsupervised clustering methods. Experimental results using software radio data demonstrate a 1/3 reduction in running time without compromising clustering accuracy at higher signal-to-noise ratios (SNR).