Description: Durable Handheld RFID Reader based on Android OS. Microsoft office for mac 2016 v15.13.3 multi [techtools.net].iso. Type: Handheld Reader, Wi-fi Reader, USB Reader. Description: Impinj Speedway Revolution R220 Uhf RFID Reader (2 Port). Type: Fixed Reader, POE Reader. In a passive ultra-high frequency (UHF) radio-frequency identification (RFID) system, tag collision is generally resolved on a medium access control (MAC) layer. However, some of collided tag signals could be recovered on a physical (PHY) layer and, thus, enhance the identification efficiency of the RFID system. For the recovery on the PHY layer, channel estimation is a critical issue. Good channel estimation will help to recover the collided signals. Existing channel estimates work well for two collided tags. When the number of collided tags is beyond two, however, the existing estimates have more estimation errors. In this paper, we propose a novel channel estimate for the UHF RFID system. It adopts an orthogonal matrix based on the information of preambles which is known for a reader and applies a minimum-mean-square-error (MMSE) criterion to estimate channels. From the estimated channel, we could accurately separate the collided signals and recover them. By means of numerical results, we show that the proposed estimate has lower estimation errors and higher separation efficiency than the existing estimates. Introduction Ultra-high frequency (UHF) radio frequency identification (RFID) is a non-contact electronic identification technology []. UHF RFID has a lot of advantages, such as long communication range, high security, large storage capacity, and so on. Additionally, it is easily integrated into enterprise management information systems. As UHF RFID is widely used in various kinds of information systems, it becomes one of key technologies to identify objects in the Internet of Things. In a passive UHF RFID system, an RFID reader identifies multiple tags on a shared wireless channel. When the multiple tags simultaneously transmit their signals to the reader, collisions will happen []. Many conventional anti-collision algorithms resolve the problem only on a media access control (MAC) layer [,,,,,,]. The algorithms consider the collided signals as useless information, so their identification efficiency is not high. In recent years, an MAC-physical (MAC-PHY) cross-layer approach [,,,,,] is introduced. The approach combines random multiple access on an MAC layer with signal separation on a PHY layer to resolve the tag collisions. The idea uses the random multiple access to prevent tag collision on the MAC layer. If there are still some collided tags, they will then be separated on the PHY layer. In the approach, the collided signals are not longer considered as useless information. Thus, the approach has higher communication efficiency than pure MAC layer methods. For the cross-layer approach, the estimation of the wireless channel coefficient is an important issue. Good channel estimation will help to correctly recover the collided tag signal on the PHY layer. However, the channel estimation in a UHF RFID system is some different from that in other wireless communication systems. First, the estimation has to be performed under unsynchronized condition. Each UHF RFID tag has different symbol period and delay []. The passive tag can not synchronize its backscattering symbols due to its simple circuit []. Second, pilot-based channel estimation can not be performed in the RFID system. Sometimes, there are no pilots at all, e.g. TRext = 0 in EPC C1 Gen2 []. Moreover, we cannot alter the pilots to adapt the channel estimation because they are pre-designed. Constellation mapping (CM) [] is an algorithm proposed to recover the collided tag signals on the PHY layer. The algorithm maps collided signals to an orthogonal/phase (IQ) plane and then recovers the mapped signals through an unsupervised clustering method. Since there is no channel estimation in the algorithm, it is actually a blind method. Its computational complexity increases with the number of the collided tags. When the number of the collided tags is beyond two, especially, the algorithm is very difficult to separate the collided signals. The single-antenna zero-forcing (SAZF) algorithm [] can also recover the collided signals on the PHY layer. The algorithm is not a blind method and, thus, has lower computational complexity since it uses the channel information. SAZF projects collided signals onto an orthogonal space of the signals and then searches an extreme value to estimate the channel. Under a single-receiving-antenna environment, however, the algorithm can estimate the channel for only two collided tags.
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