Deep Learning-based UHF RFID Tag Collision Detection Method

This paper presents a novel deep learning-based radio frequency identification (RFID) tag collision detection method for ultra-high frequency (UHF) RFID. UHF RFID technology provides longer communication range compared to NFC, barcode, and QR code technology. However, due to the longer range, multiple tags in wide range may reply simultaneously such that a reader receives superposed signal of multiple tags. Multiple tag signals interfere with each other such that reader's tag reading speed is decreased. In order to detect these tag collisions, previous studies utilized analytical methods rather than theoretical ones. Hence, a deep learning-based solution can improve the detection performance. For deep learning, training datasets are generated from mathematical equations, which are specified by the standard, with various delay times, amplitude differences, phase differences and noise level among tag signals. Arbitrary delay time, phase difference, and amplitude difference are used in every run of simulation. Simulation results show that the detection performance using the proposed method is about 5 dB better than that of existing method.