环境反向散射中面向能量检测门限的迭代参数估计方法

屈文锋; 叶迎晖; 施丽琴; 卢光跃

doi:10.11999/JEIT260418

环境反向散射中面向能量检测门限的迭代参数估计方法

doi: 10.11999/JEIT260418 cstr: 32379.14.JEIT260418

西安邮电大学通信与信息工程学院西安 710121

基金项目: 国家自然科学基金(62571430, 62471388, 62301421)，陕西省创新能力支撑计划(2024ZC-KJXX-016)

详细信息

作者简介:
屈文锋：男，硕士生，研究方向为环境反向散射通信

叶迎晖：男，副教授，硕士生导师，研究方向为无线供能通信、反向散射通信以及移动边缘计算等

施丽琴：女，副教授，硕士生导师，研究方向为无线供能通信、反向散射通信以及移动边缘计算等

卢光跃：男，教授，研究方向为通信信号处理、多天线技术以及高效频谱利用等

通讯作者:
叶迎晖　connectyyh@126.com

1¹⁾针对复高斯源和PSK源的检测门限及理论误码率，文献[15]已给出完整推导。
中图分类号: TN92
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出版历程
- 收稿日期: 2026-04-09
- 修回日期: 2026-06-29
- 录用日期: 2026-06-29
- 网络出版日期: 2026-07-13

Iterative Parameter Estimation Method for Energy Detection Threshold in Ambient Backscatter

School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China(62571430, 62471388, 62301421), The Innovation Capability Support Program of Shaanxi(2024ZC-KJXX-016)

摘要

摘要: 在环境反向散射通信(AmBC)中，阅读器利用能量检测器恢复出标签信号，其检测门限所需参数由含有标签未知符号“1”和“0”的接收信号平均功率确定。现有参数估计方法假设标签以等概率发送符号“1”和“0”，因此直接对排序后的接收信号样本功率进行等分。但标签发送的符号数有限，因此符号“1”和“0”的数量并非严格相等，此时等分会导致样本错分，使得检测门限偏离最优值。为此，该文设计了一种基于迭代更新的检测门限参数估计方法，其核心思想是利用已有的判决结果对排序样本进行重新分组，并对门限参数进行迭代更新，实现检测门限的逐步修正。为了验证所提方法的有效性，在复高斯源、PSK源和QAM源3种典型环境射频源条件下进行了仿真。仿真结果表明，该方法能有效降低门限参数估计偏差，相较于现有排序分组的参数估计方法，仅需1次迭代更新，其检测误码率在复高斯源、PSK源和QAM源下即可实现约0.1, 1.5和1.3个数量级的性能提升。
- 环境反向散射通信 /
- 符号检测 /
- 能量检测器 /
- 迭代方法
Abstract: Objective In Ambient Backscatter Communication (AmBC) systems, a low-complexity Energy Detector (ED) is commonly employed at the reader to recover symbols transmitted by the tag. The detection performance of ED depends strongly on the accurate setting of the detection threshold, which is determined by the average received signal power corresponding to tag symbols “1” and “0”. Existing parameter estimation methods assume that the two symbols are transmitted with equal probability and therefore divide the sorted received signal power samples into two equal groups. However, because the number of transmitted symbols is finite, the actual numbers of symbols “1” and “0” are generally unequal. Therefore, equal partitioning introduces sample misclassification, causing the estimated threshold to deviate from its optimal value and reducing detection performance. To address this limitation, an iterative threshold parameter estimation method is proposed to reduce the parameter estimation bias caused by sample misclassification and improve the accuracy of detection threshold estimation. Methods An iterative threshold parameter estimation method is proposed to overcome the sample misclassification introduced by conventional sorting-based grouping. Because the initial detection threshold obtained by the sorting-based grouping method provides reliable decisions for most received samples, these initial decisions are used as the basis for sample reclassification. The received signal samples are then reclassified to iteratively update the threshold parameters, progressively refining the detection threshold. The proposed method is evaluated through simulations under three representative ambient radio-frequency source conditions: complex Gaussian, Phase-Shift Keying (PSK), and Quadrature Amplitude Modulation (QAM) sources. Results and Discussions Simulation results show that, over a wide range of Signal-to-Noise Ratio (SNR) values, the proposed iterative method substantially reduces the Bit Error Rate (BER) compared with the conventional sorting-based grouping method and approaches the theoretical lower bound obtained with perfect parameter estimation. At a given SNR, the proposed method improves BER by approximately 0.1, 1.5, and 1.3 orders of magnitude under complex Gaussian, PSK, and QAM sources, respectively (Fig. 2). These results demonstrate that the proposed iterative method effectively corrects sample misclassification and reduces the performance loss caused by parameter estimation bias. Moreover, most of the performance gain is achieved after only one iteration, indicating rapid convergence with minimal additional computational overhead. Under different numbers of sampling points, BER improvements of approximately 0.5, 1.6, and 1.1 orders of magnitude are achieved under complex Gaussian, PSK, and QAM sources, respectively (Fig. 3). These results indicate that using the initial decisions for sample reclassification effectively reduces the estimation bias introduced by fixed equal partitioning, thereby improving detection performance under limited-sample conditions. Under different Relative Channel Difference (RCD) values, BER improvements of approximately 0.56 and 0.7 orders of magnitude are achieved under complex Gaussian and QAM sources, respectively (Fig. 4). As the RCD increases, the separation between the received signal power distributions becomes more pronounced, improving the accuracy of the initial decisions and enabling more reliable sample reclassification. This positive feedback process further refines the parameter estimates and improves detection performance. Conclusions An iterative threshold parameter estimation method is proposed to address the sample misclassification introduced by conventional sorting-based grouping in ED. The proposed method uses the initial decisions to reclassify the received signal samples and iteratively update the threshold parameters. In addition, closed-form expressions for the detection threshold and BER under QAM sources are derived. Simulation results demonstrate that the proposed method effectively reduces parameter estimation bias with only one iteration while maintaining robust performance under limited-sample and varying channel conditions. Significant BER improvements are achieved with minimal additional computational overhead, making the proposed method well suited for practical, high-reliability AmBC systems.
- Ambient Backscatter Communication (AmBC) /
- Symbol detection /
- Energy detector /
- Iterative threshold parameter estimation

HTML全文

图 1 AmBC系统模型图

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图 2 不同环境射频源下BER随$ \gamma $的变化

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图 3 不同环境射频源下BER随N的变化

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图 4 不同环境射频源下BER随RCD的变化

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