# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 4 Simulation Results  
In this section, the simulation results are presented to evaluate the performance of CRCScatter. We use DBPSK modulation in 802.11b transmission and set the length of frame body to 32 bits. The tag data length \(N\) and the SNR are varied to calibrate the results. First, we verify the effectiveness of the CRCScatter system. In the simulation, the SNR can be set to different values through its relationship with \(Eb/N0\). Figure 7 shows the results of the decoding bit error rate of tag data versus the SNR for different tag data lengths \(N\). The SNR varies from \(-15\) dB to \(-5\) dB, and the tag data length \(N\) is fixed as 8, 16, 24, and 32 bits. In Figure 7, we observe that the decoding the BER of the tag data can be reduced by increasing the SNR. Nevertheless, decoding the BER is similar in terms of the tag data length \(N\). From the figure, the system can achieve a BER level of \(10^{-2}\) at \(-7.5\) dB,which proves the effectiveness of the CRC reverse-algorithm-based decoding method in the low SNR regime. Moreover, the SNR does not affect the BER performance when the SNR is less than \(-13\) dB. In this case, the system cannot decode correctly due to excessive noise interference. Next, we test the decoding time of tag data using the brute-force search and the CRC reverse algorithm. Table 2 exhibits the results of the average decoding time versus different tag data lengths for the algorithms. We observe that the decoding time of brute-force search increases sharply when tag data length \(N\) increases from 4 bits to 18 bits. Due to the excessive decoding time, the brute-force method is not able to meet the requirements of the real-time communication system. Nevertheless, the tag data length \(N\) does not affect the average decoding time of the CRC reverse algorithm, which is close to \(1.0\times 10^{-2}\) s. Overall, the CRC reverse algorithm is superior to the brute-force search when the tag data length is long. With the average decoding time of the CRC reverse algorithm and the maximum tag data length, we can estimate that the maximum tag data rate of the system is 3.2 kbps, which is sufficient for intelligent meter reading systems, intelligent bracelets, and other micro IoT devices. Finally, we test the performance of using redundant bits in tag data to reduce the decoding BER. In the simulations, this study uses four duplicate parity bits as redundant bits for tag data. As shown in Figure 8a, the additional redundant bits can reduce the decoding BER when SNR is greater than \(-12\) dB. In addition, when the SNR is higher than \(-7\) dB, the improved method can achieve accurate decoding with tag data length \(N=16\) bits. With the same \(\text{SNR}=-10\) dB, Figure 8b shows that the decoding BER of tag data with redundant bits is 9% to 15% of the BER without redundant bits. These results suggest that adding redundant bits can significantly reduce the BER in the presence of noise interference. Additionally, because the average decoding time of tag data is stable and the parity check algorithm is simple, adding redundant parity check bits will not affect the efficiency of the decoding. Figure 7: The decoding BER versus SNR for different values of \(N\). The decoding BER is independent of tag data length \(N\). In summary, we validate the feasibility of CRCScatter through simulation results. In practical scenarios, such as product identification and quality control in fruit supply chain management, the CRCScatter system has the potential to be deployed on mobile devices. Tags containing key information are affixed to the surface of the fruit. By receiving and decoding the backscatter signal of the tag, managers can simultaneously obtain crucial information such as the location and type of multiple different products. Due to low hardware requirements and low energy consumption, we believe this communication