Performance Improvement of RFID System Based on Optimization Algorithm

Radio Frequency Identification (RFID) technology, as an advanced wireless communication technology, is gradually replacing barcodes as the new automatic identification standard due to its powerful data collection and object recognition capabilities. Its core advantage lies in achieving non-contact and long-distance data transmission, breaking through the limitations of line of sight in traditional recognition technology, ensuring high recognition accuracy and strong environmental adaptability, while providing high-speed data transmission rates. However, despite the many advantages of RFID technology, it still faces challenges such as signal interference and identification obstacles caused by improper tag placement in practical application scenarios, which directly affect the overall performance and reliability of the system. To address the above challenges, this article innovatively proposes an RFID tag collision avoidance algorithm based on deep learning (DL). This algorithm effectively improves the tag recognition ability of RFID systems in complex environments and reduces the occurrence of collision phenomena through the powerful processing capability of DL. The experimental results show that the algorithm not only significantly improves the accuracy and efficiency of label recognition, but also enhances the system's adaptability to environmental changes.