基于自适应压缩感知的OFDM稀疏信道估计研究
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摘要
针对OFDM(Orthogonal Frequency Division Multiplexing)系统信道稀疏且稀疏度未知的特性,提出了一种新的稀疏度自适应压缩感知信道估计方法,即弱选择分段自适应匹配追踪(Weak Selection Stagewise Adaptive Matching Pursuit,WSStAMP)算法。该算法结合了原子预选和变步长的思想,在初选阶段设置模糊阈值进行原子的预选,删去不理想的原子;又引入一个新的标识参数,通过标识参数进行每步的可变步长操作,同时文中设计了一种幂函数型的变步长方法,以克服SAMP算法固定步长导致的重建精度问题。仿真结果表明,相比于传统的自适应重建算法,文中提出的WSStAMP算法能得到更好的信道估计性能,且算法复杂度更低。
To estimate the sparse channel in orthogonal frequency division multiplexing( OFDM) system without the prior knowledge of the sparsity of the channel,a new sparsity adaptive channel estimation algorithm is proposed,called the weak selection stagewise adaptive matching pursuit( WSStAMP) algorithm.The algorithm combines the idea of atomic preselection and variable step size,sets the fuzzy threshold for the pre-selection of atoms at the primary selection stage to delete the unsatisfactory atoms and then introduces a new identification parameter to conduct the variable step operation for each step. A variable step size method based on power function is designed to solve the reconstruction accuracy problem caused by the fixed step size of SAMP algorithm. Simulation results show that the WSStAMP algorithm has better performance on the channel estimation and lower computational complexity,compared with other traditional sparsity adaptive reconstruction algorithms.
To estimate the sparse channel in orthogonal frequency division multiplexing( OFDM) system without the prior knowledge of the sparsity of the channel,a new sparsity adaptive channel estimation algorithm is proposed,called the weak selection stagewise adaptive matching pursuit( WSStAMP) algorithm.The algorithm combines the idea of atomic preselection and variable step size,sets the fuzzy threshold for the pre-selection of atoms at the primary selection stage to delete the unsatisfactory atoms and then introduces a new identification parameter to conduct the variable step operation for each step. A variable step size method based on power function is designed to solve the reconstruction accuracy problem caused by the fixed step size of SAMP algorithm. Simulation results show that the WSStAMP algorithm has better performance on the channel estimation and lower computational complexity,compared with other traditional sparsity adaptive reconstruction algorithms.
引文
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[12] TILLMANN A,PFETSCH M. The computational complexity of the restricted isometry property,the nullspace property,and related concepts in compressed sensing[J].IEEE Transactions on Information Theory,2014,60(2):1248-1259.
[13] SONG C B,XIA S T. Sparse signal recovery by?qminimization under restricted isometry property[J]. IEEE Signal Processing Letters,2014,21(9):1154-1158.
[14] ZHANG Y,VENKATESAN R,DOBRE O A,et al. An adaptive matching pursuit algorithm for sparse channel estimation[C]∥IEEE Wireless Communications and Networking Conference(WCNC). 2015:626-630.
[15] CANDES E J,WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[16] NEEDELL D,TROPP J A. Co Sa MP:iterative signal recovery from incomplete and inaccurate samples[J]. Applied&Computational Harmonic Analysis,2008,26(3):301-321.
[2] SAVAUX V,BADER C F. Mean square error analysis and linear minimum mean square error application for preamble-based channel estimation in orthogonal frequency division multiplexing/offset quadrature amplitude modulation systems[J]. IET Communications,2015,9(14):1763-1773.
[3] COLERI S,ERGEN M,PURI A,et al. Channel estimation techniques based on pilot arrangement in OFDM systems[J]. IEEE Transactions on Broadcasting,2002,48(3):223-229.
[4] MOHAMMADIAN R,AMINI A,KHALAJ B H. Compressive sensing-based pilot design for sparse channel estimation in OFDM systems[J]. IEEE Communications Letters,2017,21(1):4-7.
[5] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[6] BARANIUK R G. Compressive sensing[J]. IEEE Signal Processing Magazine,2007,24(4):118-121.
[7] WEI D,MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[8] CAI T T,WANG L. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory,2011,57(7):4680-4688.
[9] NEEDELL D,VERSHYNIN R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):310-316.
[10] DO T T,GAN L,NGUYEN N,et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]∥42nd Asilomar Conference on Signals,Systems and Computers. 2008:581-587.
[11] BI X,CHEN X,ZHANG Y,et al. Variable step size stagewise adaptive matching pursuit algorithm for image compressed sensing[C]∥IEEE International Conference on Signal Processing,Communication and Computing(ICSPCC). 2013:1-4.
[12] TILLMANN A,PFETSCH M. The computational complexity of the restricted isometry property,the nullspace property,and related concepts in compressed sensing[J].IEEE Transactions on Information Theory,2014,60(2):1248-1259.
[13] SONG C B,XIA S T. Sparse signal recovery by?qminimization under restricted isometry property[J]. IEEE Signal Processing Letters,2014,21(9):1154-1158.
[14] ZHANG Y,VENKATESAN R,DOBRE O A,et al. An adaptive matching pursuit algorithm for sparse channel estimation[C]∥IEEE Wireless Communications and Networking Conference(WCNC). 2015:626-630.
[15] CANDES E J,WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[16] NEEDELL D,TROPP J A. Co Sa MP:iterative signal recovery from incomplete and inaccurate samples[J]. Applied&Computational Harmonic Analysis,2008,26(3):301-321.
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