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  • 标题:Sparse Adaptive Channel Estimation Based on <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-6.80586pt;width:16.0875px;" id="M1" height="25.5" version="1.1" viewBox="0 0 16.0875 25.5" width="16.0875"> <g transform="matrix(.022,-0,0,-.022,.062,16.025)"><path id="x1D459" d="M238 681l-124 -585q-7 -31 4 -31q10 0 37.5 18.5t49.5 41.5l16 -22q-40 -48 -89.5 -81.5t-76.5 -33.5q-42 0 -16 122l105 488q7 32 0 41t-39 9h-35l5 26q35 3 71 13t58 17.5t26 7.5q14 0 8 -31z"></path></g> <g transform="matrix(.016,-0,0,-.016,6.088,21.4)"><path id="x1D45D" d="M570 304q0 -108 -87 -199q-40 -42 -94.5 -74t-105.5 -43q-41 0 -65 11l-29 -141q-9 -45 -1.5 -58t45.5 -16l26 -2l-5 -29l-241 -10l4 26q51 10 67.5 24t26.5 60l113 520q-54 -20 -89 -41l-7 26q38 28 105 53l11 49q20 25 77 58l8 -7l-17 -77q39 14 102 14q82 0 119 -36&#xA;t37 -108zM482 289q0 114 -113 114q-26 0 -66 -7l-70 -327q12 -14 32 -25t39 -11q59 0 118.5 81.5t59.5 174.5z"></path></g> </svg>-Norm-Penalized Affine Projection Algorithm
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  • 作者:Yingsong Li ; Wenxing Li ; Wenhua Yu
  • 期刊名称:International Journal of Antennas and Propagation
  • 印刷版ISSN:1687-5869
  • 电子版ISSN:1687-5877
  • 出版年度:2014
  • 卷号:2014
  • DOI:10.1155/2014/434659
  • 出版社:Hindawi Publishing Corporation
  • 摘要:We propose an -norm-penalized affine projection algorithm (LP-APA) for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating an -norm into the cost function of the conventional affine projection algorithm (APA) to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.
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