MCScatter: Multi-Carrier OFDM Modulation for Transmitting Tag Data over Ambient Wi-Fi Signals

Ambient backscatter communication has emerged as a promising paradigm for ultra-low-power wireless systems. To support diverse IoT applications, backscatter communication should support high uplink throughput, maintain ultra-low power consumption, and reuse existing Wi-Fi signals without special infrastructure. Achieving high-throughput backscatter over existing Wi-Fi signals, however, remains challenging because rapid fluctuation of ambient signals distort the backscattered signal, severely degrading decoding reliability and limiting throughput. Existing approaches, such as codeword translation and per-sample encoding, fail to meet all these requirements, particularly due to their limited throughput caused by single-carrier modulation and the need for additional hardware, such as dual receivers or full-duplex receivers. To address this issue, in this paper, we propose MCScatter, whereby an ultra-low-power device (ULPD) modulates its data using multi-carrier modulation and transmits data by reflecting ambient Wi-Fi signals. To mitigate distortion from ambient signal fluctuations, MCScatter estimates the effective channel, capturing both wireless propagation effect and fluctuations, and equalizes the received signal accordingly. To boost throughput, MCScatter incorporates two mechanisms: Frequency-aware Adaptive Modulation (FAM), which chooses different modulation schemes across subcarriers, and Maximum Likelihood Estimation (MLE), which enables reliable decoding of ULPD data embedded on the cyclic prefix (CP) of a Wi-Fi signal. To the best of our knowledge, MCScatter is the first design to jointly realize OFDM-based ULPD modulation with practical reuse of ambient Wi-Fi signals. To analytically characterize system behavior, we develop an analytical bit error rate (BER) model for MCScatter. Evaluation results demonstrate that MCScatter significantly outperforms benchmarks in both BER and throughput.