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ofdm--papr-BER

于 2021-02-22 发布文件大小:291KB

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下载积分: 1 下载次数: 43

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代码说明：

降低正交频分复用系统PAPR的各种算法及误码率，调试后是正确的(OFDM PAPR)

文件列表：

xhBER分析
.........\000clipping_signal .m,6495,2013-08-15
.........\000PTSclipping_signal .m,8430,2013-08-26
.........\000PTS_signal.m,8396,2013-08-16
.........\000SLM_signal .m,7593,2013-08-15
.........\01clipping-PTS-SLM-对比.m,5235,2013-08-12
.........\02PTS-different-division对比.m,6615,2013-08-09
.........\03SLM,不同M值对比.m,3277,2013-08-10
.........\04clipping,多次限幅滤波对比.m,3882,2013-08-10
.........\05只clipping，没滤波PAPR抑制.m,1381,2013-08-11
.........\06PTSclipping级联算法.m,3327,2013-08-13
.........\07PTS-clipping-PTSclipping对比.m,4629,2013-08-12
.........\08PTSclipping与clippingPTS对比.m,5498,2013-08-13
.........\09PTS-clipping-PTSclipping-clippingPTS对比.m,6461,2013-08-13
.........\BER11.fig,8324,2013-09-26
.........\BER22.fig,8383,2013-09-26
.........\BER_original_clipping_PTSclipping.asv,8301,2013-09-11
.........\BER_original_clipping_PTSclipping.m,8305,2013-09-26
.........\BER_PTSclipping_clippingPTS.asv,9866,2013-09-11
.........\BER_PTSclipping_clippingPTS.m,9870,2013-09-26
.........\clipped.m,925,2012-05-25

.........\SNR.doc,91648,2013-09-11
.........\untitled.fig,8371,2013-09-16
.........\untitled2.fig,8382,2013-09-16
.........\新BER.doc,55808,2013-09-11

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newmark
matlab程序，中心差分法。。。。。。。。。。。。。。。。。(matlab program, central difference method. . . . . . . . . . . . . . . . .)

2011-07-17 09:32:44下载

积分：1
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TOOLBOX_calib.zip. for our learning.

2011-12-12 19:53:30下载

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2011-04-07 23:59:54下载

积分：1
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2010-05-13 17:12:10下载

积分：1
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用matlab语言编写的spring embedder算法,有注释。spring embedder算法是一种基本的force-directed算法用来visualize small network(One type of force-directed algorithm, spring embedder algorithm to realize the network visualization)

2011-12-01 08:10:51下载

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2020-08-07 12:59:46下载

积分：1
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2007-12-30 18:56:42下载

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2012-07-09 09:03:38下载

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可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.)

2013-03-11 22:29:52下载

积分：1

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