Hardware co-simulation configuration for the verification of the demodulation structure
In this chapter, we present a demodulation structure suitable for a reader baseband receiver in a passive Radio Frequency IDentification (RFID) environment. RFID refers to a technology which uses radio communications to contactlessly identify a tagged physical object [1-2]. An RFID system may include a plurality of electronic tags on objects, animals, and other things having unique identification information and a reader for reading or writing information from or to the tags. RFID systems can be variously classified into the inductively coupled and electromagnetic schemes according to the communication method employed between an RFID reader and a tag, into an active type and a passive type according to whether the tag operates using its own power or not, and into long wave, medium wave, shortwave, ultrashort wave, and microwave depending on the frequency of the electric waves used for the communication [1-2]. Essentially, a passive RFID system consists of a reader and a passive tag without a battery. The International standard, ISO 18000-6C, defines the communication protocol and Ultra High Frequency (UHF) band between the reader and the passive tag . Many studies have been conducted in the field of UHF RFID, as described in [4-14]. In the case of passive UHF RFID technology, the reader must provide the tag with continuous radio power, while the tag sends its information to the reader via a backscatter modulation. The tag encodes the backscattered signal as either FM0 (bi-phase space) or Miller modulation of subcarrier at the given data rate . Recently, UHF Passive RFID has a trend of extending its domain to the application of an item-level-tagging (ILT) from that of a conventional pallet/case-level-tagging. In the ILT RFID environment, tags can be attached on the objects composed partially of a metal or liquid and can be placed at a nearby complicated surrounding in which the metallic objects exist. As a result, if undesired large signal reflected from the complicated surrounding is received at the reader receiver during receiving a desired backscattered tag signal, the performance of the identification for the reader can be easily degraded due to the reflected large signal which can leak to the reader receiver (Fig. 1(a)). In addition, if insufficient isolation is guaranteed between the transmitter and receiver, the transmission power (Tx power) created by the reader transmitter can leak to the receiver (Fig. 1(a)) . A reflected power larger than the backscattered tag signal which is generated by the return loss (S11) of the antenna can also leak to the receiver via the circulator (Fig. 1(a)). Because of these unwanted leakage components in the reader receiver, the DC-offset phenomenon can occur in the baseband of the reader receiver.
As a result, the received baseband signal can be corrupted by the DC-offset phenomenon (Fig 1(b)). For example, Fig. 1(b) shows the Miller subcarrier signal highly affected by the DC offset phenomenon in our reader receiver measured using an Agilent Logic Analyzer. Due to the unwanted DC-offset phenomenon, the reader baseband receiver may not determine the valid bit data with sufficient reliability. There have been several researches to reduce the originally generated leakage components in advance, as reported in [4-6]. However, it may be difficult to perfectly and adaptively eliminate the leakage components in the ILT RFID field, in which the performance of the reader receiver can be adversely affected by the unwanted large reflected signals. Therefore, although the received baseband signal is contaminated with the DC-offset phenomenon, we attempt to further remove the DC-offset phenomenon from the distorted received signal in the baseband receiver. In the earlier study, we proposed a demodulation structure composed of an edge signal generator, an edge extractor and a signal reconstruction block for the FM0 signal distorted by the DC-offset phenomenon . In this chapter, a similar concept is also applied to the reliable reconstruction of the Miller subcarrier signal not suffering from the DC-offset phenomenon by using the phase inversion information instead of the amplitude information. In order to accomplish this, we detect the valid information from the corrupted signals by making use of a demodulation structure composed of a peak signal generator, a peak detector, and a signal reconstruction block, in order to successfully decode the received baseband signal distorted by the DC-offset phenomenon. According to the proposed demodulation method, the peak signal is created at the position of phase inversion within the Miller subcarrier signal sequence using the phase inversion information. Therefore, although a certain amount of DC-offset noise can be appeared in the baseband of the reader receiver, the proposed method is allowed to supplementally deal with the DC-offset phenomenon once more in the baseband receiver.
This chapter is organized as follows. In Section 2, we describe in detail the demodulation structure and method used to extract meaningful information from the distorted Miller subcarrier signal suffering from the DC-offset phenomenon. In Section 3, we show the simulation and implementation results. Finally, we draw our conclusions in Section 4.
2. Demodulation algorithm
In this section, we introduce the demodulation structure and algorithm suitable for the reconstruction of the Miller subcarrier signal distorted by DC-offset noise.
2.1. Demodulation of the Miller subcarrier-encoded signal
The Miller modulated sequence contains exactly two, four, or eight subcarrier cycles per bit, depending on the M value (M = 2,4,8) specified in . Namely, if M has a value of 2, the Miller basis signal is multiplied by a square-wave at 2 times the symbol rate (1/), resulting in a Miller subcarrier signal with M = 2, as shown in Fig. 2. For the reliable reconstruction of the Miller subcarrier signal under the DC-offset environmnet, Fig. 3 shows the proposed demodulation architecture, which includes a peak signal generator, a peak extractor, and a signal reconstruction block, similar to the FM0 demodulation structure . As shown in Fig. 3, the received signal, r(t) is composed of an in-phase channel (I-channel) signal, , and a quadrature-phase channel (Q-channel) signal, including DC-offset noise, , and the complex additive noise, , which is a sample function of a white Gaussian process with power spectrum watts/hertz. At this point, the DC-offset noise () can be expressed as follows :
where is the initial DC-offset value, and represent the damping term and oscillation term of the DC-offset noise, respectively, and is the initial phase of the DC-offset noise. By adjusting the parametrs, B, and related to the DC-offset noise to proper values, any kind of DC-offset phenomenon in the area of passive RFID can be established.
In order to generate the peak signal with respect to the received baseband signal r(t) which is sampled at a sampling rate of, the initial peak signal is designed using the predefined andas follows:
where is the output signal using via the I and Q channels and is defined as follows:
where, A is the normalized gain, and is the output signal using via the I and Q channels and is defined as follows:
In the second step, the created initial peak signal is reconstructed by removing the low level noise included in the specific level of the initial peak signal. This operation is implemented in the level decision block by using a reference level (Fig. 3). The reference level for the level decision is fixed at a value of 0. This is because the two orthogonal basis functions and participate in building the peak signal through Eq. (2), while only one basis function is used for the generation of the edge signal for the case of the FM0 signal . Therefore, the demodulation method of the Miller subcarrier signal has the advantage that there is no need to find the optimal decision level, unlike the adaptive level decision method in the case of the FM0 signal. Then, the final peak signal can be obtained after the level decision by using the fixed reference level and the initial peak signal as follows:
In the next step, from the final peak signal, , the peak extractor (Fig. 3) finds the positions of the peaks using a peak detection algorithm which is identical to that of the edge extractor in Fig. 5 . Finally, in the signal reconstruction block (Fig. 3), the basedband signal without DC-offset noise is regenerated by a state diagram which is also identical to that of the signal reconstruction block in Fig. 6 . Therefore, the procedure for the proposed Miller subcarrier demodulation algorithm can be summarized as shown in Fig. 5
2.2. Determination of the low pass filter specification
In our method for the demodulation of the Miller subcarrier signal, the difference signal between and must be reformed using a low pass filter (LPF), as mentioned in
Section 2.1. Fig. 6(a) shows the spectral response of the difference signal of the Miller subcarrier signal with M = 4 before the LPF, and Fig. 6(b) represents the spectral response of the initial peak signal after the LPF. To obtain the peak signal from the difference signal before the LPF, the second harmonic component of the difference signal (Fig. 6(a)) must be attenuated below a certain level, as shown in Fig. 6(b). We observe in Fig. 7 that, as the attenuation level is increased, the error rate performance () for the Miller subcarrier signal is improved. However, the computational complexity for the design of the filter is also increased. This is because the order of the designed filter is increased to obtain the high level of the. Meanwhile, when theincrease to a value larger than about 70dB, there is no noticeable improvement of the error rate performance. From the result shown in Fig. 7, we found that a level of attenuation ranged from 70dB to 80dB can provide a suitable tradeoff between the computational complexity and the error rate performance for the demodulation algorithm. The result in Fig. 6(b) was obtained using a 39th order LPF with an attenuation level of 70dB and the magnitude response of the designed LPF is shown in Fig. 8
3. Simulation & experimental results
For the first example, we consider the operation of the proposed demodulation structure when the received baseband signal is the distorted Miller subcarrier-encoded signal with DC-offset noise (= 5, B =/, and = 1/(100)). Fig. 9 shows the demodulation result of the Miller subcarrier signal with M = 2. Case 1 of Fig. 9(a) and Case 2 of Fig. 9(b) represent the output signals in Eq. (3) and in Eq. (4), respectively. From the results, we observe that both output signals, and, are robust to the variation of the amplitude, although the I- and Q-channel received baseband signals are severely distorted by DC-offset noise. Fig. 9(c) represents the difference signal between and and the corresponding initial peak signal after the LPF. A 54th order LPF is designed for the generation of the initial peak signal, in order to attenuate the amplitude response of the filter to about 80dB. The spectral responses of the difference signal before the LPF and the initial peak signal after the LPF, and the magnitude response of the designed 54th order LPF are shown in Fig. 10. Fig. 9(d) represents the final peak signalusing the fixed reference level of 0 and the initial peak signal. Note that the generated peaks of are placed at every position at which a phase inversion occurs within the Miller subcarrier signal sequence. Finally, as shown in Fig. 9(e), the reconstructed baseband signal without the DC-offset noise is obtained using the peak extraction algorithm of the peak extractor and the state diagram of the signal reconstruction block. The resulting signal in Fig. 9(e) has the same form as the Miller basis signal, which is obtained by removing the rectangular subcarrier signal from the Miller subcarrier signal, as shown in Fig. 2. This is because the peak signal, which appears at a position having phase inversion, is used to reconstruct the Miller baseband signal.
Case 1: the output signal in Eq. (3) using
Case 2: the output signal in Eq. (4) using
Case 3: the difference signal between and before the 39th order LPF having an attenuation level of 70dB
Case 4: initial peak signal after the 39th order LPF
Case 5: final peak signal using the fixed reference level and the initial peak signal
Case 6: Reconstructed Miller baseband signal without DC-offset noise
In the next example, we implemented the proposed demodulation structure as a hardware device FPGA (Field Programmable Gate Array) and then the operation of the demodulation structure is observed using the commercial DSP design tool, Xilinx System Generator which provides hardware co-simulation, making it possible to incorporate the demodulation design running in an FPGA directly into a MATLAB Simulink simulation . Fig. 12 shows the designed hardware co-simulation model of the proposed demodulation structure using a Black Box and JTAG Co-Sim library block provided by the System Generator. The Black Box library block allows a designed HDL (hardware description language), such as VHDL and Verilog, to be brought into the Simulink design model and enables us to easily observe the corresponding simulation behaviour in MATLAB Simulink.
|System Generator||Xilinx System Generator 10.1|
|HDL design tool||Xilinx ISE 10.1|
|HDL simulation tool||ModelSim SE 6.2b|
|FPGA (Digital hardware board)||Xilinx SPARTAN-3 XC3S4000FGG676-5G|
|JTAG Cable||Xilinx USB cable|
|Simulink system period [sec]||3.90625e-7 (1/2.56MHz)|
Meanwhile, the Black Box HDL can be co-simulated with MATLAB Simulink using the System Generator interface to either ISE simulator or the ModelSim simulation software from Model Technology, Inc. Fig. 13 shows the operation result of the demodulation structure using the latter method through ModelSim when the measured Miller subcarrier signal (Fig. 2(b)) is considered. The operation result (Scope 2) using the former method through ISE simulator is also shown in Fig. 14
To obtain the JTAG Co-Sim library block (Fig. 12) for the demodulation structure, the hardware and software required to run the hardware co-simulation on the FPGA board (Table 1) should be installed and set up in advance . Therefore, once the System Generator has successfully finished compiling the HDL design into the FPGA bitstream, it automatically creates the JTAG Co-Sim library block as shown in Fig. 12. As a result, in comparison with Fig. 14, we observe that the timing simulation result of Fig. 15 is nearly identical to the functional HDL simulation result of Fig. 14.
Finally, the measured operation results obtained using Agilent Logics Analyzer equipment are described in Fig. 16. From the results of Figs. 13, 14, 15, and 16, we observe that the proposed method can successfully reconstruct the Miller baseband signal, even though the received Miller subcarrier signal is distorted by the DC-offset phenomenon. Although we do not show the corresponding demodulation results, the demodulation structure and algorithm described in Section 2 can be directly used to reconstruct a distorted Miller subcarrier signal with M = 8.
In this study, we propose a reader baseband receiver structure for the demodulation of the Miller subcarrier signal described in the international UHF RFID standard, 18000-6 Type C, under the DC-offset phenomenon. In order to perfectly remove the DC-offset noise caused by the leakage components in a passive RFID environment, the proposed structure for the passive RFID reader baseband receiver primarily includes the peak signal generator, the peak extractor, and the signal reconstruction block for demodulating Miller subcarrier signals. The phase inversion information is used to generate the peak signal and the resulting reconstructed signal is the Miller baseband signal removed both the DC-offset noise and the rectangular subcarrier signal simultaneously. To verify the operation and functionality of the proposed demodulation structure, we implemented both the HDL co-simulation using the Black Box library block and the hardware co-simulation using the JTAG Co-Sim one simultaneously. The results show that the proposed demodulation method can successfully extracts the valid information from the signal corrupted by the DC-offset noise which can occur in a passive RFID configuration.
This work was supported by the IT R&D program of MKE/IITA [Development of Next Generation RFID Technology for Item Level Applications], Rep. of Korea.