Error Resilient Estimation and Adaptive Binary Selection for Fast and Reliable Identification of RFID Tags in Error-Prone Channel

Abstract

In RFID systems, far field passive tags send information using back scattering. The signal level is typically very small, so channel error during transmission may occur frequently. Due to channel error, performance of RFID tag identification under error-prone channel is degraded compared to that under error-free channel. In this paper, we propose a novel error resilient estimation and adaptive binary selection to overcome the problem of channel errors. Our proposed error resilient estimation algorithm can estimate the number of tags and the channel state accurately regardless of frame errors. And our proposed adaptive binary selection reduces the idle slots caused by frame errors. Performance analysis and simulation results show that the proposed algorithm consumes up to 20 percent less time slots than the binary tree protocol and dynamic framed slotted ALOHA (DFSA) in various packet error rate (PER) conditions.