ResNet-Enhanced DFSA: A Time-Efficient UHF RFID Inventory System for Large-Scale Applications

In dense RFID environments, simultaneous responses from multiple tags during reader identification attempts result in signal collisions that compromise successful tag detection. These collisions, predominantly arising from the mismatch between allocated time slots and actual tag population, significantly degrade identification efficiency. The EPC Gen2 protocol employs Dynamic Frame Slotted Aloha (DFSA) to dynamically adjust frame sizes across inventory rounds, yet existing implementations lack precise tag quantity estimation during collision events. This paper presents an AI-enhanced collision resolution framework where a ResNet-based classifier analyzes collided RN16 signals to estimate concurrent responders (up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex> tags). By integrating this real-time collision cardinality prediction with DFSA's slot adaptation mechanism, our approach achieves up to 93.3 % time saving than conventional implementations through optimized frame size adjustments. The proposed methodology demonstrates particular efficacy in dynamic and dense deployments (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;100$</tex> tags), establishing a machine learning paradigm for physical-layer signal processing in RFID anticollision protocols.