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改进的OPAST算法及其在盲多用户检测中的应用

Improved OPAST algorithm and its application in the blind multiuser detection
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摘要 文中详细地介绍了正交投影子空间跟踪算法(OPAST),它是一种基于最优化问题的方法,保证了每次迭代时权向量的正交性,并具有和PAST算法一样的线性复杂度,以及与自然幂法(NP)一样的全局收敛性。然而将其应用于盲多用户检测时,在迭代一定次数后,会出现误码率突然增大现象,这就导致了算法性能的下降,为了解决这一问题,文中提出一种方法,并通过仿真结果,证明它是行之有效的。 The orthonormal projection approximation and subspace tracking algorithm (OPAST) is introduced. It is a kind of method based on the theory of optimum. It guarantees the orthonormality of the weight matrix at each iteration. Moreover it has a linear complexity like the PAST algorithm and a global convergence property like the natural power (NP) method. However it can appear suddenly increase ber phenomenon after a certain numbem of iteration, when it is applied to the blind muhiuser detection.It lead to decline in the performance.In order to solve this problem, this paper puts forward a method, and prove it is effective through the results of the simulation.
出处 《电子设计工程》 2012年第13期101-103,107,共4页 Electronic Design Engineering
关键词 OPAST算法 盲多用户检测 自适应算法 误码率 OPAST algorithm blind multiuser detection adaptive algorithm BER
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参考文献5

  • 1钱林杰,程翥,石斌斌,刘海涛,万建伟.一类子空间跟踪方法的改进[J].信号处理,2010,26(5):741-745. 被引量:2
  • 2Yang B. Projection approximation subspace tracking [J]. IEEE Trans Signal Processing, 1995 (44) : 95-107.
  • 3HUA Y,CHEN T,MIAO Y. A unifying view of a class of subspace trac king methods [J]. In ISSPR' 98,1998 (2) : 27-32.
  • 4Wang X,Poor H V. Blind muhiuser detection: A subspace app- roach[J]. IEEE Trans. Inform.Theory, 1998,44(2) :667-691.
  • 5Yang B. Projection approximation subspace tracking [J]. IEEE Trans Signal Processing, 1995 (43):95-107.

二级参考文献10

  • 1M. Moonen, P. Van Dooren, J. Vandewalle. Updating singular value decompositions: A parallel implementations [C]. Proc. SPIE Adv. Alg. Archi. Sig. Proc. , 1989, 80-91.
  • 2P. A. Thompson. An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise[C]. Proc. 13^th Asilomar Conf. Circuits, Syst. Comput. , Pacific Grove, CA, 1980.
  • 3J. Yang, M. Kaveh. Adaptive eigensubspace algorithms for direction or frequency estimation and tracking [ J ]. IEEE Trans Acoust, Speech, Signal Processing, 1988, 36(2) :241-251.
  • 4E. Oja. Principal components, minor components, and linear neural networks [ J ]. Neural Networks, 1992, 5 (Dec) :927-935.
  • 5X. G. Doukopoulos, G. V. Moustakides. The fast data projection method for stable subspace tracking [ C ]. in 13^th Europ. Signal Process. Conf., EUSIPCO'2005, Antalya, Turkey, Sep. 2005.
  • 6S. Bartelmaos, K. Abed-Meraim. Principal and minor subspaee tracking: Algorithms & stability analysis [ C]. in Proc. IEEE ICASSP'06, Toulouse, France, 2006, III: 560-563.
  • 7S. Bartelmaos and K. Abed-Meraim. An efficient & stable algorithm fcr minor subspace tracking and stability analysis [ C ]. IEEE Int. Conf. Acoustics, Speech, Signal Processing(ICASSP), Honolulu, HI, Apr. 2007, vol. III: 1301-1304.
  • 8X. G. Doukopoulos, G. V. Moustakides. Fast and Stable Subspace Tracking [J].IEEE Trans. Signal Process., Apr. 2008,56(4) : 1452-1465.
  • 9R. Badeau, G. Richard, and B. David. Fast and Stable YAST Algorithm for Principal and Minor Subspace Tracking [ J ]. IEEE Trans Signal Process, 2008, 56 ( 8 ) : 3437 -3446.
  • 10L. Yang, S. Attallah, G. Mathew, and K. A. Meraim. Analysis of Orthogonality Error Propagation for FRANS and HFRANS Algorithms [ J ]. IEEE Trans Signal Process, 2008, 56(9) : 4515-4521.

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