Iterative Parameter Estimation Method for Energy Detection Threshold in Ambient Backscatter

QU Wenfeng; YE Yinghui; SHI Liqin; LU Guangyue

doi:10.11999/JEIT260418

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QU Wenfeng, YE Yinghui, SHI Liqin, LU Guangyue. Iterative Parameter Estimation Method for Energy Detection Threshold in Ambient Backscatter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260418

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QU Wenfeng, YE Yinghui, SHI Liqin, LU Guangyue. Iterative Parameter Estimation Method for Energy Detection Threshold in Ambient Backscatter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260418

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Iterative Parameter Estimation Method for Energy Detection Threshold in Ambient Backscatter

doi: 10.11999/JEIT260418 cstr: 32379.14.JEIT260418

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)

Received Date: 2026-04-09
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-29

Available Online: 2026-07-13

Abstract

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

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References(20)

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