Ambient backscatter communication (AmBC) enables ambient Internet of Things (AIoT) devices to achieve ultra-low-power, low-cost, and massive connectivity. Most existing AmBC studies assume ideal synchronization between the backscatter device (BD) and the backscatter receiver (BR). However, in practice, symbol timing offset (STO) occurs due to both the propagation delay and the BR activation latency, which leads to unreliable symbol recovery at the BR. Moreover, the uncontrollable nature of the ambient radio frequency source renders conventional correlation-based synchronization methods infeasible in AmBC. To address this challenge, we investigate STO estimation and symbol detection in AmBC without requiring coordination from the ambient radio frequency source. Firstly, we design a specialized pilot sequence at the BD to induce sampling errors in the pilot signal. Furthermore, we propose a pilot-based STO estimator using the framework of maximum likelihood estimation (MLE), which can exploit the statistical variations in the received pilot signal. Finally, we integrate STO compensation into an energy detector and evaluate the bit error rate (BER) performance. Simulation results show that the proposed estimator achieves accurate STO estimation and effectively mitigates the BER performance degradation caused by STO.

翻译：环境反向散射通信（AmBC）使环境物联网（AIoT）设备能够实现超低功耗、低成本和大规模连接。现有的大多数AmBC研究假设反向散射设备（BD）与反向散射接收器（BR）之间存在理想的同步。然而，在实际中，由于传播延迟和BR激活延迟，会产生符号定时偏移（STO），这导致BR端的符号恢复不可靠。此外，环境射频源的不可控特性使得传统的基于相关的同步方法在AmBC中不可行。为了应对这一挑战，我们研究了在无需环境射频源协调的情况下，AmBC中的STO估计与符号检测。首先，我们在BD端设计了一个专门的导频序列，以在导频信号中引入采样误差。此外，我们基于最大似然估计（MLE）框架提出了一种基于导频的STO估计器，该估计器能够利用接收导频信号中的统计变化。最后，我们将STO补偿集成到能量检测器中，并评估了误码率（BER）性能。仿真结果表明，所提出的估计器能够实现精确的STO估计，并有效缓解由STO引起的BER性能下降。