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fengjunbi

于 2014-08-17 发布文件大小:4138KB

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目前已经提出了许多不同的算法来解决OFDM 系统中的PAPR 问题，本章详细介绍了其中的限幅法、压缩扩张法、星座图扩展法、选择性映射法和部分传输序列法。在介绍每一种算法时，针对其主要存在的问题进行了相应的改进，使每一种算法都成为比较实用的算法。(Has put forward a number of different algorithms to solve the problem PAPR OFDM system, this chapter details Fine describes one of the limiting, compression expansion method, constellation expansion method, selective mapping method and partial transmission Sequence method. In introducing each algorithm, for which the main problem for the corresponding improvements, so that each An algorithm have become more practical algorithms.)

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fengjunbi.docx,4647990,2014-07-24

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The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)

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