Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.

Extracting data bits from bistatic-backscattered WiFi signals by threshold-based decoding is challenging due to severe fluctuation of OFDM signals. We propose adaptive transmission repetition to combat this problem. The key idea is to let a backscatter transmitter repeat transmissions and a receiver combines those so that signal fluctuation is filtered out due to its time-changing patterns, as observed in our testbed experiments, thus reducing bit error rate. However, excessive transmission repetitions lead to the waste of channel time, thus we propose an algorithm to adapt the number of repetitions so as to maximize the effective throughput in the present communication condition. We demonstrate via testbed experiments that the proposed algorithm adapts well to various communication conditions and achieves near-best throughput performance.

In WiFi backscatter communication, the frequency shift technique allows a backscattered signal to appear not in the frequency channel of the carrier signal but in adjacent ones, thus avoiding noisy OFDM-based carrier signals and increasing the communication range. Through testbed experiments, we observe that frequency shift is effective in mitigating the impact of the inherent fluctuation of WiFi signals, particularly in bistate backscatter communication; however, due to the weak strength of the backscattered signal, other signals from incumbent transmitters may appear in the shifted frequency channels, significantly interfering with the backscattered signal. To combat this challenge in a way that is nondisruptive to incumbent transmitters, we propose a receiver-side spectro-temporal combining scheme in which spectrum combining is performed to suppress interference appearing in one of the shifted channels, while temporal combining is performed with transmission repetitions to suppress bit errors resulting from residual interference. The scheme’s on-the-fly spectrum combining and bit-sequence temporal combining require minimal buffer memory. Through system prototyping and testbed experiments, we demonstrate that the proposed scheme outperforms the conventional and temporal-combining-only cases in terms of the bit error rate and throughput under various conditions.