智能反射面輔助的環(huán)境反向散射通信系統(tǒng)信道估計(jì)算法研究

徐勇軍; 邱友靜; 張海波

doi:10.11999/JEIT240395

智能反射面輔助的環(huán)境反向散射通信系統(tǒng)信道估計(jì)算法研究

doi: 10.11999/JEIT240395

重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(62271094, U23A20279)，重慶市自然科學(xué)基金重點(diǎn)項(xiàng)目(CSTB2022NSCQ-LZX0009, CSTB2023NSCQ-LZX0079)，重慶市教委科技重點(diǎn)項(xiàng)目(KJZD-K202200601)

詳細(xì)信息

作者簡(jiǎn)介:
徐勇軍：男，教授，博士生導(dǎo)師，研究方向?yàn)榉聪蛏⑸渫ㄐ?、智能反射面、信道估?jì)、資源分配等

邱友靜：女，碩士生，研究方向?yàn)榉聪蛏⑸渫ㄐ?、智能反射面、信道估?jì)等

張海波：男，副教授，碩士生導(dǎo)師，研究方向?yàn)橘Y源分配、反向散射通信、車聯(lián)網(wǎng)等

通訊作者:
徐勇軍　xuyj@cqupt.edu.cn

中圖分類號(hào): TN929.5
計(jì)量
- 文章訪問(wèn)數(shù): 248
- HTML全文瀏覽量: 53
- PDF下載量: 46
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-05-20
- 修回日期: 2024-11-15
- 網(wǎng)絡(luò)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: 環(huán)境反向散射通信(AmBC)是一種新型的低功耗通信技術(shù)，它能利用周圍環(huán)境中的射頻(RF)信號(hào)源實(shí)現(xiàn)無(wú)源信息傳輸，但由于其存在雙重衰落、障礙物阻擋等問(wèn)題，導(dǎo)致反射鏈路信號(hào)強(qiáng)度弱。為此，該文將智能反射面(IRS)引入到AmBC系統(tǒng)中用以增強(qiáng)反射鏈路增益。然而，IRS與標(biāo)簽均為無(wú)源器件使得信道估計(jì)極具挑戰(zhàn)性。為此，該文提出了一種IRS輔助的AmBC系統(tǒng)信道估計(jì)方案。首先，將信道分解為多個(gè)子信道，其中，反射鏈路的每個(gè)子信道對(duì)應(yīng)一個(gè)IRS反射單元。然后，將最小二乘(LS)法作為估計(jì)準(zhǔn)則，以最小化均方誤差(MSE)為目標(biāo)，探索了IRS反射模式與信道估計(jì)的聯(lián)合設(shè)計(jì)。仿真結(jié)果表明，該信道估計(jì)方案具有良好的估計(jì)性能。
- 環(huán)境反向散射通信 /
- 智能反射面 /
- 信道估計(jì)
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

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圖 1 IRS輔助的AmBC系統(tǒng)

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圖 2 直連信道均方誤差隨信噪比變化曲線

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圖 3 級(jí)聯(lián)信道均方誤差隨信噪比變化曲線

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圖 4 直連信道均方誤差隨反射單元數(shù)變化曲線

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圖 5 級(jí)聯(lián)信道均方誤差隨反射單元數(shù)變化曲線

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圖 6 直連信道均方誤差隨反射單元數(shù)變化曲線

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圖 7 級(jí)聯(lián)信道均方誤差隨反射單元數(shù)變化曲線

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