Fast OFDM Wi-Fi Backscatter Systems Based on Composite Channel Decoupling

Improving transmission efficiency is a key objective in OFDM WiFi backscatter systems. A promising direction is sub-symbol-level tag modulation, which embeds more tag data within each OFDM symbol. However, we observe that fine-grained tag modulation is coupled with channel variation, which distorts the cascade structure between the two channels, transmitter-to-tag and tag-to-receiver, making the conventional channel estimation method in WiFi ineffective. Although recent systems have explored new channel estimation methods, their accuracy is limited and the modulation redundancy remains necessary. To address this problem, we present Fascatter, a high-throughput OFDM WiFi backscatter system that enables single-sample-level tag modulation without modulation redundancy. The key enabler is a new channel estimation method that independently estimates the two channels at per-subcarrier granularity. We construct channel observations from the LTF fields and reference symbols, and accurately solve the two channels through matrix decomposition. We further introduce polynomial smoothing and multi-symbol fine-tuning modules to improve estimation robustness. Experimental results demonstrate that the channel estimation results are close to the actual channel responses, and our method shows robust performance under a variety of complex channel conditions. In particular, Fascatter achieves a throughput of up to 15.9 Mbps, which is at least 3.2× over state-of-the-art systems. © 2026 IEEE.