智能反射面辅助的环境反向散射通信系统信道估计算法研究

徐勇军; 邱友静; 张海波

doi:10.11999/JEIT240395

智能反射面辅助的环境反向散射通信系统信道估计算法研究

doi: 10.11999/JEIT240395 cstr: 32379.14.JEIT240395

重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家自然科学基金(62271094, U23A20279)，重庆市自然科学基金重点项目(CSTB2022NSCQ-LZX0009, CSTB2023NSCQ-LZX0079)，重庆市教委科技重点项目(KJZD-K202200601)

详细信息

作者简介:
徐勇军：男，教授，博士生导师，研究方向为反向散射通信、智能反射面、信道估计、资源分配等

邱友静：女，硕士生，研究方向为反向散射通信、智能反射面、信道估计等

张海波：男，副教授，硕士生导师，研究方向为资源分配、反向散射通信、车联网等

通讯作者:
徐勇军　xuyj@cqupt.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 687
- HTML全文浏览量: 269
- PDF下载量: 87
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-20
- 修回日期: 2024-11-15
- 网络出版日期: 2024-12-12
- 刊出日期: 2025-01-31

Channel Estimation for Intelligent Reflecting Surface Assisted Ambient Backscatter Communication Systems

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62271094, U23A20279), The Key Fund of Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009, CSTB2023NSCQ-LZX0079), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601)

摘要

摘要: 环境反向散射通信(AmBC)是一种新型的低功耗通信技术，它能利用周围环境中的射频(RF)信号源实现无源信息传输，但由于其存在双重衰落、障碍物阻挡等问题，导致反射链路信号强度弱。为此，该文将智能反射面(IRS)引入到AmBC系统中用以增强反射链路增益。然而，IRS与标签均为无源器件使得信道估计极具挑战性。为此，该文提出了一种IRS辅助的AmBC系统信道估计方案。首先，将信道分解为多个子信道，其中，反射链路的每个子信道对应一个IRS反射单元。然后，将最小二乘(LS)法作为估计准则，以最小化均方误差(MSE)为目标，探索了IRS反射模式与信道估计的联合设计。仿真结果表明，该信道估计方案具有良好的估计性能。
- 环境反向散射通信 /
- 智能反射面 /
- 信道估计
Abstract: Objective Ambient Backscatter Communication (AmBC) is an emerging, low-power, low-cost communication technology that utilizes ambient Radio Frequency (RF) signals for passive information transmission. It has demonstrated significant potential for various wireless applications. However, in AmBC systems, the reflected signals are often severely weakened due to double fading effects and signal obstruction from environmental obstacles. This results in a substantial reduction in signal strength, limiting both communication range and overall system performance. To address these challenges, Intelligent Reflecting Surface (IRS) technology has been integrated into AmBC systems. IRS can enhance reflection link gain by precisely controlling reflected signals, thereby improving system performance. However, the passive nature of both the IRS and tags makes accurate channel estimation a critical challenge. This study proposes an efficient channel estimation algorithm for IRS-assisted AmBC systems, aiming to provide theoretical support for optimizing system performance and explore the feasibility of achieving high-precision channel estimation in complex environments—key to the practical implementation of this technology. Methods This study develops a general IRS-assisted AmBC system model, where the system channel is divided into multiple subchannels, each corresponding to a specific IRS reflection element. To minimize the Mean Squared Error (MSE) in channel estimation, the Least Squares (LS) method is used as the estimation criterion. The joint optimization problem for channel estimation is explored by integrating various IRS reflection modes, including ON/OFF, Discrete Fourier Transform (DFT), and Hadamard modes. The communication channel is assumed to follow a Rayleigh fading distribution, with noise modeled as zero-mean Gaussian. Pilot signals are modulated using Quadrature Phase Shift Keying (QPSK). To thoroughly evaluate the performance of channel estimation, 1000 Monte Carlo simulations are conducted, with MSE and the Cramer-Rao Lower Bound (CRLB) serving as performance metrics. Simulation experiments conducted on the Matlab platform provide a comprehensive comparison and analysis of the performance of different algorithms, ultimately validating the effectiveness and accuracy of the proposed algorithm. Results and Discussions The simulation results demonstrate that the IRS-assisted channel estimation algorithm significantly improves performance. Under varying Signal-to-Noise Ratio (SNR) conditions, the MSE of methods based on DFT and Hadamard matrices consistently outperforms the ON/OFF method, aligning with the CRLB, thereby confirming the optimal performance of the proposed algorithms (Fig. 2, Fig. 3). Additionally, the MSE for direct and cascaded channels is identical when using the DFT and Hadamard methods, while the cascaded channel MSE for the ON/OFF method is twice that of the direct channel, highlighting the superior performance of the DFT and Hadamard approaches. As the number of IRS reflection elements increases, the MSE for the DFT and Hadamard methods decreases significantly, whereas the MSE for the ON/OFF method remains unchanged (Fig. 4, Fig. 5). This illustrates the ability of the DFT and Hadamard methods to effectively exploit the scalability of IRS, demonstrating better adaptability and estimation performance in large-scale IRS systems. Furthermore, increasing the number of pilot signals leads to a further reduction in MSE for the DFT and Hadamard methods, as more pilot signals provide higher-quality observations, thereby enhancing channel estimation accuracy (Fig. 6, Fig. 7). Although additional pilot signals consume more resources, their substantial impact on reducing MSE highlights their importance in improving estimation precision. Moreover, under high-SNR conditions, the MSE for all algorithms is lower than that under low-SNR conditions, with the DFT and Hadamard methods showing more pronounced reductions (Fig. 4, Fig. 5). This indicates that the proposed methods enable more efficient channel estimation under better signal quality, further enhancing system performance. In conclusion, the channel estimation algorithms based on DFT and Hadamard matrices offer significant advantages in large-scale IRS systems and high-SNR scenarios, providing robust support for optimizing low-power, low-cost communication systems. Conclusions This paper presents a low-complexity channel estimation algorithm for IRS-assisted AmBC systems based on the LS criterion. The channel is decomposed into multiple subchannels, and the optimization of IRS phase shifts is designed to significantly enhance both channel estimation and transmission performance. Simulation results demonstrate that the proposed algorithm, utilizing the DFT and Hadamard matrices, achieves excellent performance across various SNR and system scale conditions. Specifically, the algorithm effectively reduces the MSE of channel estimation, exhibits higher estimation accuracy under high-SNR conditions, and shows low computational complexity and strong robustness in large-scale IRS systems. These results provide valuable insights for the theoretical modeling and practical application of IRS-assisted AmBC systems. The findings are particularly relevant for the development of low-power, large-scale communication systems, offering guidance on the design and optimization of IRS-assisted AmBC systems. Additionally, this work lays a solid theoretical foundation for the advancement of next-generation Internet of Things applications, with potential implications for future research on IRS technology and their integration with AmBC systems.
- Ambient Backscatter Communication (AmBC) /
- Intelligent Reflecting Surface (IRS) /
- Channel estimation

HTML全文

图 1 IRS辅助的AmBC系统

下载: 全尺寸图片幻灯片

图 2 直连信道均方误差随信噪比变化曲线

下载: 全尺寸图片幻灯片

图 3 级联信道均方误差随信噪比变化曲线

下载: 全尺寸图片幻灯片

图 4 直连信道均方误差随反射单元数变化曲线

下载: 全尺寸图片幻灯片

图 5 级联信道均方误差随反射单元数变化曲线

下载: 全尺寸图片幻灯片

图 6 直连信道均方误差随反射单元数变化曲线

下载: 全尺寸图片幻灯片

图 7 级联信道均方误差随反射单元数变化曲线

下载: 全尺寸图片幻灯片

参考文献(26)

[1]	XU Yongjun, GUI Guan, GACANIN H, et al. A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668–695. doi: 10.1109/COMST.2021.3059896.
[2]	张晓茜, 徐勇军. 面向零功耗物联网的反向散射通信综述[J]. 通信学报, 2022, 43(11): 199–212. doi: 10.11959/j.issn.1000-436x.2022199. ZHANG Xiaoxi and XU Yongjun. Survey on backscatter communication for zero-power IoT[J]. Journal on Communications, 2022, 43(11): 199–212. doi: 10.11959/j.issn.1000-436x.2022199.
[3]	XU Yongjun, XIE Hao, WU Qingqing, et al. Robust max-min energy efficiency for RIS-aided HetNets with distortion noises[J]. IEEE Transactions on Communications, 2022, 70(2): 1457–1471. doi: 10.1109/TCOMM.2022.3141798.
[4]	GALAPPATHTHIGE D L, REZAEI F, TELLAMBURA C, et al. RIS-empowered ambient backscatter communication systems[J]. IEEE Wireless Communications Letters, 2023, 12(1): 173–177. doi: 10.1109/LWC.2022.3220158.
[5]	LE A T, NGUYEN T N, TU L T, et al. Performance analysis of RIS-assisted ambient backscatter communication systems[J]. IEEE Wireless Communications Letters, 2024, 13(3): 791–795. doi: 10.1109/LWC.2023.3344113.
[6]	张晓茜, 徐勇军, 吴翠先, 等. 智能反射面增强的全双工环境反向散射通信系统波束成形算法[J]. 电子与信息学报, 2024, 46(3): 914–924. doi: 10.11999/JEIT230356. ZHANG Xiaoxi, XU Yongjun, WU Cuixian, et al. Beamforming design for reconfigurable intelligent surface enhanced full-duplex ambient backscatter communication networks[J]. Journal of Electronics & Information Technology, 2024, 46(3): 914–924. doi: 10.11999/JEIT230356.
[7]	YANG Hancheng, DING Haiyang, CAO Kunrui, et al. A RIS-segmented symbiotic ambient backscatter communication system[J]. IEEE Transactions on Vehicular Technology, 2024, 73(1): 812–825. doi: 10.1109/TVT.2023.3306037.
[8]	MA Shuo, WANG Gongpu, FAN Rongfei, et al. Blind channel estimation for ambient backscatter communication systems[J]. IEEE Communications Letters, 2018, 22(6): 1296–1299. doi: 10.1109/LCOMM.2018.2817555.
[9]	ZHAO Wenjing, WANG Gongpu, ATAPATTU S, et al. Blind channel estimation in ambient backscatter communication systems with multiple-antenna reader[C]. Proceedings of 2018 IEEE/CIC International Conference on Communications in China, Beijing, China, 2018: 320–324. doi: 10.1109/ICCChina.2018.8641171.
[10]	ZHU Yue, WANG Gongpu, TANG Hengliang, et al. Channel estimation for ambient backscatter systems over frequency-selective channels[C]. Proceedings of 2018 IEEE/CIC International Conference on Communications in China, Beijing, China, 2018: 384–388. doi: 10.1109/ICCChina.2018.8641250.
[11]	LIU Xuemeng, LIU Chang, LI Yonghui, et al. Deep residual learning-assisted channel estimation in ambient backscatter communications[J]. IEEE Wireless Communications Letters, 2021, 10(2): 339–343. doi: 10.1109/LWC.2020.3030222.
[12]	ABDALLAH S, SALAMEH A I, and SAAD M. Joint channel, carrier frequency offset and I/Q imbalance estimation in ambient backscatter communication systems[J]. IEEE Communications Letters, 2021, 25(7): 2250–2254. doi: 10.1109/LCOMM.2021.3075493.
[13]	ABDALLAH S, VERBOVEN Z, SAAD M, et al. Channel estimation for full-duplex multi-antenna ambient backscatter communication systems[J]. IEEE Transactions on Communications, 2023, 71(5): 3059–3072. doi: 10.1109/TCOMM.2023.3251387.
[14]	CUI Ziqi, WANG Gongpu, WEI Xusheng, et al. Channel estimation and optimal training design for ambient backscatter communication systems under sensitivity constraint[C]. Proceedings of 2022 IEEE 96th Vehicular Technology Conference, London, United Kingdom, 2022: 1–5. doi: 10.1109/VTC2022-Fall57202.2022.10012695.
[15]	CUI Ziqi, WANG Gongpu, GAO Jie, et al. Channel estimation for backscatter communication systems under circuit sensitivity constraint[J]. IEEE Transactions on Vehicular Technology, 2024, 73(5): 7441–7446. doi: 10.1109/TVT.2023.3347926.
[16]	ABEYWICKRAMA S, YOU Changsheng, ZHANG Rui, et al. Channel estimation for intelligent reflecting surface assisted backscatter communication[J]. IEEE Wireless Communications Letters, 2021, 10(11): 2519–2523. doi: 10.1109/LWC.2021.3106165.
[17]	LIN Junliang, WANG Gongpu, XU Rongtao, et al. Versatile-modulation and megabit-rate backscatter system: Design, implementation, and experimental results[J]. IEEE Internet of Things Journal, 2024, 11(5): 8240–8252. doi: 10.1109/JIOT.2023.3318634.
[18]	LI Dong. Two birds with one stone: Exploiting decode-and-forward relaying for opportunistic ambient backscattering[J]. IEEE Transactions on Communications, 2020, 68(3): 1405–1416. doi: 10.1109/TCOMM.2019.2957490.
[19]	XU Yongjun, GU Bowen, HU R Q, et al. Joint computation offloading and radio resource allocation in MEC-based wireless-powered backscatter communication networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 6200–6205. doi: 10.1109/TVT.2021.3077094.
[20]	D’AMICO A A and MORELLI M. Symbol-spaced feedforward techniques for blind bit synchronization and channel estimation in FSO-OOK communications[J]. IEEE Transactions on Communications, 2024, 72(1): 361–374. doi: 10.1109/TCOMM.2023.3317931.
[21]	GU Bowen, LI Dong, LIU Ye, et al. Exploiting constructive interference for backscatter communication systems[J]. IEEE Transactions on Communications, 2023, 71(7): 4344–4359. doi: 10.1109/TCOMM.2023.3277519.
[22]	XIE Ning, XU Yuntao, ZHANG Jiaheng, et al. Joint estimation of channel responses and phase noises in asynchronous MIMO systems with intentional timing offset[J]. IEEE Transactions on Communications, 2023, 71(1): 412–426. doi: 10.1109/TCOMM.2022.3223711.
[23]	SENGIJPTA S K. Fundamentals of statistical signal processing: Estimation theory[J]. Technometrics, 1995, 37(4): 465–466. doi: 10.1080/00401706.1995.10484391.
[24]	MISHRA D and JOHANSSON H. Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer[C]. Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, 2019: 4659–4663. doi: 10.1109/ICASSP.2019.8683663.
[25]	JENSEN T L and DE CARVALHO E. An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator[C]. Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 2020: 5000–5004. doi: 10.1109/ICASSP40776.2020.9053695.
[26]	ZHOU Zhengyi, GE Ning, WANG Zhaocheng, et al. Joint transmit precoding and reconfigurable intelligent surface phase adjustment: A decomposition-aided channel estimation approach[J]. IEEE Transactions on Communications, 2021, 69(2): 1228–1243. doi: 10.1109/TCOMM.2020.3034259.