The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventional data detectors face limitations due to their inadequate knowledge of channel and RF-source parameters. To address this challenge, we propose an innovative approach using a deep neural network (DNN) for channel state estimation (CSI) and signal detection within AmBC systems. Unlike traditional methods that separate CSI estimation and data detection, our approach leverages a DNN to implicitly estimate CSI and simultaneously detect data. The DNN model, trained offline using simulated data derived from channel statistics, excels in online data recovery, ensuring robust performance in practical scenarios. Comprehensive evaluations validate the superiority of our proposed DNN method over traditional detectors, particularly in terms of bit error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method exhibits an impressive approximately 20% improvement in BER performance compared to the maximum likelihood (ML) approach. These results underscore the effectiveness of our developed approach for AmBC channel estimation and signal detection. In summary, our method outperforms traditional detectors, bolstering the reliability and efficiency of AmBC systems, even in challenging channel conditions.

翻译：无处不在且价格低廉的无线连接时代为无数实际应用打开了大门。在此背景下，环境反向散射通信（AmBC）脱颖而出，它利用无源标签通过反射周围环境射频（RF）信号来建立与阅读器的连接。然而，传统数据检测器因对信道和射频源参数了解不足而面临局限性。为解决这一挑战，我们提出了一种创新方法，利用深度神经网络（DNN）在AmBC系统中进行信道状态估计（CSI）和信号检测。与将CSI估计和数据检测分离的传统方法不同，我们的方法利用DNN隐式估计CSI并同时检测数据。该DNN模型使用基于信道统计生成的模拟数据进行离线训练，在在线数据恢复方面表现出色，确保了实际场景中的稳健性能。全面评估验证了我们提出的DNN方法优于传统检测器，特别是在误码率（BER）方面。在高信噪比（SNR）条件下，与最大似然（ML）方法相比，我们的方法在BER性能上实现了约20%的显著提升。这些结果凸显了我们所开发方法在AmBC信道估计和信号检测中的有效性。总之，我们的方法优于传统检测器，增强了AmBC系统的可靠性和效率，即使在具有挑战性的信道条件下也是如此。