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项目(No.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、背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、背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[16]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: 2026-04-08
- 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(No.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，而等分会导致样本错分，使得检测门限偏离最优值。为此，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源三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，仅需一次迭代更新，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 is typically employed by the reader to recover the unknown symbols transmitted by the tag. However, achieving optimal energy detection performance critically depends on the precise setting of the detection threshold. The specific parameters of this threshold are determined by the average powers of the received signals when symbols “1” and “0” are respectively transmitted by the tag. Existing parameter estimation methods assume an equal transmission probability for symbols “1” and “0”"; therefore, the sorted received signal powers are directly divided into two equal groups. Nevertheless, due to the finite number of transmitted symbols, the actual quantities of symbols “1” and “0” are not strictly equal. Consequently, this equal division introduces sample misclassification, causing the estimated threshold to deviate from its optimal value and thereby degrading the overall detection performance of the system. Given that an absolute parity between transmitted symbols “1” and “0” cannot be guaranteed in practical AmBC systems, the traditional sorting-based grouping method fails to satisfy the accuracy requirements of highly reliable communications. Therefore, a parameter estimation method capable of eliminating sample misclassification bias is proposed, which is of paramount importance for breaking through the performance bottlenecks of existing energy detection and facilitating the practical application of AmBC technology. Methods To address the misclassification defect inherent in traditional sorting-based grouping parameter estimation methods, a detection threshold parameter estimation method based on iterative updating is proposed. Given that the detection threshold obtained via the sorting-based grouping method can already effectively determine the majority of correct symbols, these initial decision results are extracted as a highly reliable classification basis. Subsequently, the received signal sample set is reclassified to iteratively update the parameters, thereby achieving a progressive correction of the detection threshold. To comprehensively verify the effectiveness and applicability of the proposed iterative estimation method, simulation analyses are conducted under three typical ambient radio frequency source conditions: complex Gaussian, phase-shift keying (PSK), and quadrature amplitude modulation (QAM) sources. Results and Discussions Simulation results indicate that under various signal-to-noise ratio (SNR) conditions, compared with the traditional non-iterative sorting-based grouping method, the bit error rate (BER) is significantly reduced and the theoretical lower bound under perfect parameters is approached by the proposed iterative method. At a given SNR, significant performance improvements of approximately 0.1, 1.5, and 1.3 orders of magnitude are achieved by the iterative method under complex Gaussian, phase-shift keying (PSK), and quadrature amplitude modulation (QAM) sources, respectively (Fig. 2). It is demonstrated by this result that misclassified samples are effectively corrected, and the performance loss caused by estimation deviations is reduced by the proposed iterative method. Most importantly, an extremely fast convergence speed is exhibited; a significant performance improvement is obtained with merely the first iteration, which proves that high-precision parameter estimation can be accomplished with extremely low computational overhead. Secondly, under varying numbers of sampling points, BER performance improvements of approximately 0.5, 1.6, and 1.1 orders of magnitude are obtained by the iterative method under complex Gaussian, PSK, and QAM sources, respectively (Fig. 3). It is demonstrated that by utilizing the decision results as a classification basis, the sample size used for parameter estimation is equivalently expanded in a statistical sense. The irreplaceable “compensation” value of the iterative method in small-sample constrained scenarios is thereby strongly confirmed. Finally, under different relative channel difference (RCD) conditions, performance improvements of approximately 0.56 and 0.7 orders of magnitude are realized by the iterative method under complex Gaussian and QAM sources, respectively (Fig. 4). It is revealed by the analysis that as the RCD increases, the degree of separation between the received signal power distributions is enhanced; thus, a solid basis for iterative grouping is provided by the high accuracy of the initial decision. Consequently, a stable positive feedback updating process is formed, and the system detection performance is effectively improved. Conclusions To address the issue that the traditional sorting-based grouping parameter estimation method in energy detectors is prone to introducing misclassification bias and degrading detection performance, an iterative updating-based detection threshold parameter estimation method is innovatively proposed. By this method, the decision results obtained from initial parameter estimation are utilized as a reliable classification basis to regroup the received samples, thereby realizing the iterative updating of the detection threshold. Concurrently, the exact theoretical closed-form expressions for the detection threshold and bit error rate under quadrature amplitude modulation sources are derived. It is demonstrated by simulation results that an extremely fast convergence speed is achieved by the proposed method, whereby the parameter estimation bias is effectively eliminated with merely a single iteration. Moreover, excellent robustness is exhibited under small-sample constraints and varying channel conditions. With extremely low additional computational overhead, BER performance improvements by orders of magnitude are achieved by this iterative method. Consequently, a solution with significant engineering application value is provided for the physical layer design of future high-performance and highly reliable practical ambient backscatter communication systems.
- Ambient backscatter communications (AmBC) /
- Symbol detection /
- Energy detector /
- Iterative method

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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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