TagEcho: Empowering Ambient LoRa Backscatter Tags With Adaptive Modulation

LoRa backscatter communication combines the long-range and low-power advantages of LoRa with the ultra-low-energy characteristics of backscatter, making it a promising solution for large-scale IoT connectivity. However, existing designs are limited by rigid chirp-based modulation, which forces all devices to use full-length chirps regardless of channel conditions. This rigidity results in inefficient spectrum utilization and higher packet loss, particularly in dense deployments where the lack of efficient control signaling worsens coordination. In this work, we propose a technical scheme called TagEcho, which introduces an adaptive modulation framework that dynamically adjusts symbol durations to balance communication reliability and throughput. Its core design consists of two parts: (i) a frequency-hopping-based control-information embedding mechanism (detailed in Sec. III-B.3); and (ii) two adaptive modulation modes, namely multi-chirp and sub-chirp (summarized in Sec. III-E and supported by the receiver in Sec. III-C.5). These modulation modes do not disrupt the temporal structure of the signal and can flexibly adapt to changes in the channel environment to achieve finer-grained modulation. We implement a complete hardware prototype and conduct extensive evaluations. Results show that TagEcho achieves reliable communication over 3.1 km, delivers single-tag throughput up to 3906 bps, and consistently outperforms the baseline TagLite system in both indoor and outdoor scenarios, demonstrating its practicality for next-generation low-power wide-area networks. © 2026 IEEE.