## 5 Experimental Results
### Performance Evaluation for MIMO Wi-Fi Backscatter System  
In order to compare the BER performance of the proposed MIMO Wi-Fi backscatter system with the power level modulation method [12], we applied the ED method used in the existing system [12] to two systems and used only one antenna for the receiver. The BER performance of the existing Wi-Fi backscatter systems [9; 12] and the proposed MIMO Wi-Fi Backscatter system are shown in Figure 4. Figure 4a shows the performance of the proposed backscatter method adopting the first method to increase the data rate in Equations (5)-(8). An existing method [9] detected the channel variation using a threshold value obtained from the average energy, and performance degradation occurred under high signal to noise ratio (SNR) even with lower data transmission ability. The existing 4-level method [12] had the same data rate as the proposed backscatter method because it transmitted two bits per one symbol by using power-level modulation (backscattering). However, since the reflection gain of \(\sqrt{1/3}\) and \(\sqrt{2/3}\) were added to the original gain values, the reflection effects of the tag sensor for Wi-Fi RF radio waves decreased. As a result, the CSI patterns of backscatter symbols were only a little different from one another, which caused performance degradation to occur. Unlike power-level modulation method [12], tag sensor in the proposed system affected effectively the channel environments with multiple antennas' reflection whose gain was the maximum. Therefore, the presented backscatter methods showed a better BER performance than both algorithms [9; 12] regardless of the distance between the reader and the tag sensor. As shown in Figure 4a, the proposed algorithm outperformed the existing 4-level scheme by about 10 dB. Figure 4b shows the BER performance of the proposed backscatter method working as the second mode (refer to Equation (11)) to enhance the reliability (with respect to the BER performance). In this test, the existing method adopting power-level modulation [12] utilized the only 2-level gain (the minimum and the maximum gains) for matching the data speed with the proposed system. Unlike 4-level modulation, the existing method with 2-level modulation showed a good BER performance compared to the other existing method [9] due to the demodulation method with the Euclidean distance. However, this method still cannot defeat the proposed algorithm, since the two antennas of the tag sensor in the proposed system can reflect Wi-Fi RF radio waves simultaneously and the effects on the communication channel between the helper and the reader were much bigger. As shown in Figure 4b, the proposed algorithm had the same BER of \(10^{-3}\) under the lower SNR by 5 dB and 3 dB than the existing methods [9] and [12], respectively. The throughput of existing and the proposed backscatter systems is shown in Figure 5. The throughput is defined by  
\[\text{throughput}=(1-P_{FER})\times\alpha\ \ \ \left(\alpha=\log_{2}L\ k\!bps \right) \tag{21}\]  
where \(P_{FER}\) is a frame error rate (FER). In this paper, the FER was obtained when setting a frame length to 128 bits. In Wi-Fi backscatter systems, error correction cannot be performed like radio frequency identification (RFID) because the complicated block cannot be embedded in a tag sensor due to the limited size and the power consumption. Therefore, even the error of a bit in a frame causes the entire error of the frame. As shown in Figure 5, under low SNR conditions (lower than 10 dB), none of the systems can transmit frames without errors because frame errors are even caused by a few error bits. When increasing SNR values, the throughput of the proposed system increased dramatically over other algorithms and shows the maximum performance for SNR conditions over 30 dB regardless of the distance between the reader and the tag sensor. The existing algorithm [9] had a half throughput performance of the proposed system even showing the maximum performance at SNR = 30 dB.