# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## 6 Experimental Evaluation
### Experimental Results  
Figure 9 shows a signal trace of the experiment where the backscatter tag transmits the shown stream of bits at 100 kbps; each slot is 10 us long and the Wi-Fi frame lasts 1.4 ms. Thus, the backscatter tag transmits up to 70 information bits (140 coded bits) on the frame. It also shows more details about where bit errors occur. In the raw samples of the received signal, it is hard to notice the amplitude patterns corresponding to the bit stream. After smoothing, the received signal now shows noticeable amplitude patterns. The bits with decoding error are shown as red in the figure; four bit errors occurred in the sliding method, one bit error occurred in the total mean method, and no bit error occurred in the pattern-based method. In slot 83, in which a bit error occurred for both threshold-based methods, the smoothed signal had a convex downward shape, but more samples were above both thresholds and demodulated as data one. On the other hand, the pattern-based method checks the two adjacent slopes. Since slope[83] is negative, the bit pair is judged as (1, 0), and data zero is the candidate for bit[83]. Since slope[84] is positive, the bit pair is detected as (0, 1); thus, both slopes confirm that bit[83] is data zero. Figure 10 shows the comparison of BER results between decoding algorithms for different tag distances. In the figures, it is clearly shown that the proposed decoding algorithm outperforms threshold-based ones in most positions for all bit-rate cases. In particular, the proposed algorithm achieves a BER of 2.5\(e^{-4}\), 1.1\(e^{-3}\), 6.5\(e^{-3}\) and 5.7\(e^{-2}\) at 2.5 m for 20, 50, 100 and 200 kbps, respectively, which is a reduction by up to 93.2% from conventional ones. At 0 m, all algorithms achieved almost identical BER results since it has the best conditions for communication (except for the case of 200 kbps, where the proposed algorithm achieved BER reduction by 84.8%). The results show the trend that BER increases as the backscatter tag moves farther from the transmitter/receiver; we have the smallest BER at 0 m (at most, 8.7\(e^{-4}\)) and higher BERs in farther positions from it. We also see that BERs at a higher bit rate are higher than those at a lower bit rate. The highest gain is achieved as reduction by 94.4% from the BER of the sliding method at 1 m.  
Figure 8: Tag reflection gain. Figure 9: Illustration of the decoding result of different methods for an example Wi-Fi frame with backscattering at a bit rate of 100 kbps. In Figure 11, the distributions of slopes for different bit pairs at 100 kbps are shown at 0 and 2.5 m. At 0 m, bit pairs have distinct slope distributions. All slope values of bit pair (1, 0) are negative, and those of bit pair (0, 1) are all positive. The slope values of bit pairs (1, 1) and (0, 0) are distributed around zero, but do not overlap with the distributions of bit pairs (1, 0) and (0, 1). Therefore, the proposed algorithm clearly differentiates bit pairs and achieves near-zero bit error at 0 m. At 2.5 m, however, the slope distributions of different bit pairs have much overlap with each other. Some slope  
Figure 10: Bit error rate (BER) results for different tag positions. values of bit pairs (1, 0) and (0, 1) appear positive and negative, respectively, thus leading to wrong decoding results and high BER at 2.5 m. When the bit rate is doubled as 200 kbps, the slope distributions of different bit pairs become less distinguishable between them, as shown in Figure 12. We also observed that the slope values of 200 kbps were more dispersed than those of 100 kbps. This is because signal fluctuation is less filtered out due to a smaller smoothing window size.