13.1 SUMMARY OF THE PROPOSED TECHNIQUES IN PART II

Part II of this book presents advanced yet computationally feasible tag detection techniques for chipless RFID systems that are capable of improving the tag reading accuracy, reading range, and data bit capacity. Detection error rate of a number of likelihood-based detectors was presented and compared against the threshold-based detector used in existing chipless RFID systems. It is evident that all of the likelihood-based detectors perform better than the popular threshold-based detector. The performance improvement of the proposed tag detection techniques can be interpreted as an increased tag reading accuracy at a given signal-to-noise ratio (SNR) level. On the other hand, it can also be represented as an increment in the reading range while achieving a particular goal of reading accuracy. Therefore, the improved performance can be represented either as increased reading accuracy or reading range depending on the application requirement. In addition, two approaches have been taken to improve the tag data bits. First, the proposed tag detection techniques have allowed to remove the guard-band present in the frequency-domain tags. It has been shown that it allows to increase the data capacity by a factor up to 2. The second approach is to design a new MIMO chipless RFID tag and the relevant signal processing techniques. Theoretically, it can be proved that the tag data capacity can be improved by a factor of 2 or greater.

However, there is a common drawback of all the likelihood-based detection methods discussed so far. All these methods require higher computation complexity compared to the primitive detection techniques such as threshold-based detection. Computationally feasible two tag detection techniques have been proposed to reduce the computation complexity from exponential to linear in Chapter 11. It was found that the bit-by-bit detection method, which is a suboptimal detection method, performs successfully when a resonator guard-band is used in tag design. It was shown that the computation time has significantly dropped compared to exhaustive maximum likelihood detection methods without compromising the reading accuracy. In addition, a fully optimal trellis-tree-based Viterbi decoding technique has been introduced to reduce the computation complexity from exponential to linear order while achieving a similar reading accuracy to original likelihood detection techniques. Chapter 12 presents signal processing techniques for successfully detecting the tag bits of a MIMO-based chipless RFID system. First, zero forcing decomposing techniques were used on the received data array at the reader to estimate the tag response. Then two methods were used to detect the tag data bits encoded in the MIMO tag. In the first method [1], tag responses were generated in MATLAB using bandstop filters. The first method uses a threshold-based valley detection method. The other method uses the proposed ML detection technique to detect the tag bits in each branch of the MIMO chipless tag. An experiment was setup to test the ML detection technique and it was shown that the encoded tag data bits were identified successfully. A comprehensive analysis was performed using a CST- and MATLAB-based simulation. The results show that the proposed detection technique provides better detection error rate performance at different SNR values over traditional threshold-based detection. This benefit can be interpreted in two different metrics. First, it can be seen as an SNR gain over the existing threshold-based detection technique, which effectively increases the reading range. Second, the high accuracy in tag reading avoids multiple reading cycles, which yields an energy-efficient reading method.

Theoretically, higher number of branches can encode higher tag data bits within the same frequency band. However, the number of receiving antennas used in the reader should be always equal to or higher than the total branches in the MIMO tag to successfully decompose the signals. In addition, due to short distance, the proposed setup operates under line-of-sight (LOS) MIMO. In LOS MIMO, the physical distances between antennas have to be maintained such that they will not form similar channel gains between transmitter and receiver antennas. As a result, the maximum number of branches allowed in the tag is limited. Main drawback of ML detection technique is the computation complexity. It was demonstrated in Chapter 11 that the proposed computationally feasible detection techniques reduce the complexity from exponential to linear order without compromising the tag reading accuracy.

After analyzing the simulations, it is noteworthy to pinpoint that, even though there are only two transmitting branches present in the RFID tag considered, it is theoretically possible to add more branches and still recover the transmitted signals given that the number of receiving antennas in the reader is larger than or equal to the number of transmitting branches in the tag. Hence, without increasing the bandwidth, the bit capacity can be further increased using the same frequency resonators compared with having only onebranch at the tag. However, it is required to evaluate the effect of mutual coupling between antennas with higher number of transmitting branches in the tag.

In the proposed RFID tag, there is only one receiving antenna through which the received signal will be divided into two equal components. The proposed concept can be extended to having a dedicated receiving antenna for each component, hence increasing the effective SNR at each branch. Therefore, the performances can be further improved with the usage of multiple dedicated transmitting and receiving antennas on the tag. In addition, the concept can be further extended to multiple tag detection if each branch is considered as a separate tag. Furthermore, the use of I/Q modulation/demodulation allows an extra degree of freedom to increase the bit capacity. Since the baseband signal considered is complex, it is possible to have asymmetric frequency response in positive and negative frequencies. Therefore, the eligible frequency band in the passband centered around the RF carrier doubles, allowing more resonators to be placed in the tag, without increasing the sampling rate of the ADC at the receiving end of the reader. After analyzing the above results, it can be concluded that MIMO is a competitive candidate for improving reliability or the bit capacity of a resonator-based chipless RFID system.

It was found that the proposed tag detection techniques for single input single output (SISO) systems provide significantly higher tag reading accuracy over the existing threshold-based detector. In addition, they are capable of operating without a guard-band, which makes the tag data bit capacity to be doubled without compromising the reading accuracy. Moreover, the effective SNR gain provided by the proposed techniques can be represented as increasing tag reading range. All these benefits are achieved without compromising the low computation complexity. The MIMO tag with two branches is capable of encoding up to four times the total bits stored in existing SISO tags. Due to highly reliable tag detection techniques, chipless RFID tag readers do not need to read the same tag multiple times unlike the existing readers. This introduces the new ONE TIME tag reading philosophy.

13.2 LIMITATIONS OF THE PROPOSED SYSTEM

However, the performance of the proposed tag detection method could be limited by few factors. One of the main factors is the fabrication defects such as the dielectric constant of the substrate and the precision of the line widths. Due to these inaccuracies in tag design, two tags with the same tag data bits could have slightly different tag responses. These imperfections could affect the successful tag detection rate. In addition, when the tags are fabricated on paper, the resonance level is less compared to that on substrates. As a result, it could be moresusceptible to noise conditions, which can cause the detection error rate to be increased.

The tag detection technique presented in System Model IV involves estimating the channel and then using the estimated channel for tag detection. Due to various conditions such as interference and object movement, the channel may change suddenly. The proposed tag detection technique assumes a slowly varying channel for the interrogation period, which is in the order of few hundreds of milliseconds. The sudden changes of the channel conditions introduce an error in channel estimation. This error can cause the detection error rate to be increased.

The proposed tag detection techniques perform well when there is only one tag in the vicinity of the reader interrogation zone. If there are multiple tags inside the interrogation zone, the responses from other tags interfere with the current tag of interest. In order to eliminate this interference, the channel realizations from all the tags to the reader should be known. However, obtaining these channel state information is very difficult as the positions of the channel are unknown and there is no feasible way to estimate the channel from each of those tags to the reader. If the tag positions are known, the reader can interrogate by beamforming only one tag at a time and record the tag response and estimate the channel as in one tag situation.

The tag detection techniques proposed in Part II require extra computational power compared to low-spec microcontrollers used in some of the chipless RFID readers. As a result, unless the existing hardware performance is already enough, there is a hardware upgrade for the new detection techniques to be worked. However, it can be seen that this hardware upgrade is feasible with single board computers as discussed in Section 13.4.

Even under the limitations presented earlier, the proposed smart tag detection techniques are expected to increase the data bit capacity in chipless RFID tags that can be detected at a higher success rate and be detected farther away from the reader. These advances in knowledge are expected to produce commercialized chipless RFID systems in future.

13.3 POTENTIAL APPLICATIONS

The proposed tag detection techniques can be used in a number of potential applications. The most favorable would be conveyer belt applications when a tag is either printed directly on the product or the already printed tag is stuck on the product. On a conveyer belt, the items to be identified can be controlled to appear one after the other. This avoids multiple tags being illuminated by the reader at the same time, hence interference-limited tag reading can be performed. In addition, this controlled item movement is important to avoid disorientation of items as tag reading is orientation sensitive. Some of these applications can be found in production lines in manufacturing industries, packaging, pharmaceuticals, and airport luggage tracking andhandling.

Another potential application is to identify counterfeit bank notes. The tag will be printed on the polymer note with an invisible conductive ink using an inkjet or laser printer. A chipless RFID reader with the proposed tag detection techniques can be used to interrogate the banknotes and based on the detected data bits, counterfeit notes can be identified. Reserve bank of Australia is the world leader in printing polymer-based bank notes. They are currently working with the main author's research group to investigate the feasibility of implementing this technology to the bank notes.

Smart library is a concept that has been proposed for some time now. A smart library automates several day-to-day tasks with the use of RFID systems. The most popular task is the lending, where user picks a book and can check out using the RFID readers available at a self-checkout desk. This has already been realized on several occasions and the proposed detection techniques can help reliably perform several other tasks such as receiving new stocks, carrying out inventory checks, checking for misfiled items. For example, checking for misfiled items can be performed by scanning the book with a tag printed on it using a handheld chipless RFID reader.

It has been discussed about applying chipless RFID in retail market for a decade or so. The cost of fabricating a chipless RFID tag is less than a fraction of a cent, which makes it an ideal technology for tagging low-cost items (US $1 bread) in the retail market such as supermarket. Current limitations for the deployment are the low number of tag data bit and being unable to read multiple tags simultaneously. The proposed techniques help to double the data capacity by removing the guard-band and with improved tag detection techniques. So these techniques help to move one step closer to actual deployment.

There are few other areas where the proposed chipless RFID systems can be deployed. One of them is vehicle tracking where a tag is placed on the windscreen of the vehicle and readers are mounted at the entrance to the carpark. The authors collaborated with a ski center already performed a trial to check the feasibility of tracking the incoming and outgoing cars to the car park of the ski center. Tagging and tracking of individual components used in safety critical applications is another arena where the proposed chipless RFID system can be utilized. Some recommendations and open issues are presented next.

13.4 FUTURE WORK AND OPEN ISSUES

The verification of the proposed tag detection techniques was performed as a postprocessing exercise using MATLAB. Implementation of the detection techniques as a firmware is a significant step in producing commercialized chipless RFID readers having extra benefits summarized in the previous section. Table 13.1 outlines the specification of the latest Raspberry Pi 2 Model B that costs less than US $45 off the shelf.

Single board computers are becoming powerful than ever and are capable of loading advanced operating systems such as Windows or Linux. The quad-core processor and 1 GB of RAM make it possible to run powerful signal processing applications such as MATLAB. However, there are open-source software tools such as Octave, which is highly compatible with running MATLAB codes. In addition, extended 40-pin general-purpose input/output (GPIO) allows to capture signals for real-time data processing. These advances in technology and the cheap price have enabled single board computers to be a potential candidate for preparing portable chipless RFID readers with advanced tag detection techniques.

The second part of the work presented in Part II focuses on tag detection techniques for MIMO-based chipless RFID systems. The proposed detection techniques require accurate channel state information. More advanced signal processing techniques are required to be developed that perform when perfect channel state information is not available. In addition, the detection techniques were derived based on the assumption that the noise has identically independent Gaussian distributed samples. Even though the results produced are improved, it might be worth investigating a novel model to represent noise encountered in the proposed system. Moreover, the interrogating signal used is constructed based on having equal power across the frequency band of interest. However, once channel state information is available, the interrogating signal shape can be optimized to improve the tag reading accuracy.

With the proposed tag detection techniques, it is believed that the challenges for commercializing chipless RFID systems will be successfully overcome. As a result, chipless RFID can be made to be the future of barcode as the researchers predicted about a decade ago.

REFERENCE

1. 1. C. Divarathne and N. Karmakar, “MIMO based chipless RFID system,” in RFID-Technologies and Applications (RFID-TA), 2012 IEEE International Conference on, Nov 2012, pp. 423–428.