Next-Generation RFID Collision Decoding Using I/Q Constellation Geometry

Collisions caused by simultaneous tag responses are a fundamental challenge in radio frequency identification (RFID) systems, limiting their throughput and scalability. Existing solutions often rely on complex signal processing or hardware modifications, reducing practicality. This article presents a novel collision resolution algorithm, fully compatible with the EPC Gen2 standard, that decodes individual tag responses by analyzing I/Q constellation patterns formed during collisions. The method introduces a new constellation cluster labeling strategy inspired by geometric alignment from computer vision, which uses analytical geometry and pattern matching techniques to efficiently resolve tag states without requiring retransmissions, channel estimation, or hardware changes. The algorithm reliably resolves up to four colliding tags and achieves up to a 33% relative gain in time-normalized throughput over framed slotted ALOHA (FSA). To support next-generation protocol enhancements, we also propose a multitag acknowledgment configuration, where the algorithm achieves up to a 121% relative gain, with peak performance at a slot-to-tag ratio (STR) of 0.5. Moreover, the algorithm achieves a $25\times $ speedup in decoding time compared to the latest state-of-the-art method, significantly enhancing its practicality for real-time deployment. These results demonstrate the method’s effectiveness across both current and next-generation RFID systems.