智能城市应用

基于多维矩阵分解的QSNP-LS算法的无线信号接收机

虞欣, 韩曦, 王瑞炜, 刘奕晨, 万继银

摘要


针对无人机群双跳模型下的通信系统，文中提出了一种基于多维矩阵的快启动嵌套并行最小二乘法(Quick Start Nested Parallel Least Square，QSNP-LS)接收机。该方法由多维矩阵建模、信号估计方法和可辨识性条件三部分组成。在发送端处，所提方法将经过Khatri-Rao空时编码的信号发送至无人机群，无人机群对接收的信号进行放大转发至基站，利用多维矩阵结构，在基站处形成嵌套多维矩阵模型，基于此模型实现了符号和信道的联合估计，中继处无需对信号进行处理，减轻了中继处的负担。仿真结果表明，与一些竞争算法相比，所提方法在误码率(Bit Error Rate，BER)和归一化均方误差(Normalized Mean Square Error，NMSE)方面具有显著的优越性。

关键词


多维矩阵；联合估计；无人机

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参考


Y. Kawamoto, H. Nishiyama, N.Kato,F. Ono and R. Miura, "Toward Future Unmanned Aerial Vehicle Networks: Architecture, Resource Allocation and Field Experiments[J].in IEEE Wireless Communications,2019,26(1):94-99.

Zhang, Rui, Zeng, et al. Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges[J].IEEE Communications Magazine Articles News & Events of Interest to Communications Engineers,2016(9):45.

B,Li,Z,Fei,Y.Secure UAV Communication Networks over 5G[J].in IEEE Wireless Communications,2019,26(5):114-120.

L.Cheng,G.Yue,X.Xiong,Y.Liang and S. Li, "Tensor Decomposition-Aided Time-Varying Channel Estimation for Millimeter Wave MIMO Systems[J].in IEEE Wireless Communications Letters,2019(8):1216-1219.

P.R.B.Gomes,G.Fodor,W.C.Freitas,A.L.F.de Almeida and Y. C. B. Silva, "Tensor-Based Modeling and Processing for Channel Estimation in Two-Hop V2X MIMO Systems[J].2019 IEEE Conference on Standards for Communications and Networking (CSCN),2019(9):1-7.

Kong.Optimal design of source and relay pilots for MIMO relay channel estimation[J].IEEE Trans Signal Proces,2011(8):4438-4446.

H.Q.Ngo.EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays[J].2012 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP),2012(9):3249-3252.

X.Han,Y,Zhao,J.Ying."Semi-Blind Receivers for UAV M-KRST Coding MIMO Systems Based on Nested Tensor Models[J].IEEE Wireless Communications Letters,2021(1):185-188.

Sidiropoulos Giannakis.Blind PARAFAC receivers for DS-CDMA systems[J].IEEE Trans Signal Process,2000(48):810-823.

N,D. Sidiropoulos, R. Bro, and G. B. Giannakis, “Parallel factor analysis in sensor array processing[J].IEEE Trans. Signal Process,2000(48):2377-2388.

L.De Lathauwer J.Castaing, “Blind identification of underdetermined mixtures by simultaneous matrix diagonalization[J].IEEE Trans. Signal Process,2008,3(56):1096-1105.

K,Mokios,N,D.Sidiropoulos, and A. Potamianos “Blind speech separation using[J].IEEE Trans. Signal Process,2018,3(6):96-97.

余华东.基于稀疏分量分析的雷达信号欠定盲源分离方法研究[D].哈尔滨:哈尔滨工程大学,2019.

韩曦,周迎春,赵欣远,等.CCFD中继系统中基于多维矩阵的信道估计方法[J].华南理工大学学报：自然科学版,2020,48(1):7.

韩曦,赵雨雨,刘芹,等.基于PARAFAC分解的通信系统信道估计方法[J].现代信息科技,2020,4(2):71-73.

刘进,亢艳芹,顾云波,等.稀疏张量约束的低剂量CT图像重建[J].光学学报,2019,39(8):167-176.

赵文达,赵建,续志军.基于结构张量的变分多源图像融合[J].物理学报,2013,62(21):164-170.

叶学义,王大安,宦天枢,等.基于张量的2D-PCA人脸识别算法[J].计算机工程与应用,2017,53(6):1-6.

董超,徐宁,Kim Batselier,等.基于TTr1SVD的张量奇异值分解及其在人脸识别上的应用[J].计算机应用研究,2018,35(1):287-304.

计雨含,王晓东,姚宇.基于张量奇异值分解的人脸识别方法[J].计算机应用,2015,35(1):117-126.

L.R.Ximenes,G.Favier,and A.L.F.de Almeida, “Semi-blind receivers for non-regenerative cooperative MIMO communications based on nested PARAFAC modeling[J].IEEE Trans. Signal Process,2015,18(63):4985-4998.

D.Nion and N.D.Sidiropoulos, "Adaptive Algorithms to Track the PARAFAC Decomposition of a Third-Order Tensor[J]. in IEEE Transactions on Signal Processing,2009,6(57):2299-2310.




DOI: https://doi.org/10.33142/sca.v5i6.7655

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