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Likelihood

LDPC碼譯碼相關文獻 Bounds on the maximum Likelihood decoding error probability of low density parity check

LDPC碼譯碼相關文獻 Bounds on the maximum Likelihood decoding error probability of low density parity check codes

標簽： probability Likelihood decoding maximum

上傳時間： 2015-11-25

上傳用戶：wendy15
％ This routine provides a convenient way to produce Pd/FAD information ％ from Likelihood ratio info

％ This routine provides a convenient way to produce Pd/FAD information ％ from Likelihood ratio information.

標簽： information convenient Likelihood provides

上傳時間： 2016-01-05

上傳用戶：liglechongchong
A general decision rule for stochastic blind maximum-Likelihood OSTBC detection is derived.

A general decision rule for stochastic blind maximum-Likelihood OSTBC detection is derived.

標簽： maximum-Likelihood stochastic detection decision

上傳時間： 2013-12-04

上傳用戶：xcy122677
A stack-based sequential depth-first decoder that returns Maximum-Likelihood solutions to spherical

A stack-based sequential depth-first decoder that returns Maximum-Likelihood solutions to spherical LAST coded MIMO system-type problems

標簽： Maximum-Likelihood stack-based depth-first sequential

上傳時間： 2013-12-20

上傳用戶：hebmuljb
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum Likelihood form

he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum Likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.

標簽： equivalent Likelihood algorithm Sejnowski

上傳時間： 2016-09-17

上傳用戶：Altman
Maximum Likelihood Methods in Radar Array Signal Processing

Maximum Likelihood Methods in Radar Array Signal Processing

標簽： Likelihood Processing Maximum Methods

上傳時間： 2013-12-29

上傳用戶：dave520l
This demo shows the BER performance of linear, decision feedback (DFE), and maximum Likelihood seque

This demo shows the BER performance of linear, decision feedback (DFE), and maximum Likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.

標簽： performance Likelihood decision feedback

上傳時間： 2013-11-25

上傳用戶：1079836864
Implements Maximum Likelihood estimation of beta and other parameters for model of stock portfolio v

Implements Maximum Likelihood estimation of beta and other parameters for model of stock portfolio vs. index using kalman filter

標簽： Implements Likelihood estimation parameters

上傳時間： 2013-12-28

上傳用戶：zhangzhenyu
基于數據符號同步的FPGA仿真實現

近年來，人們對無線數據和多媒體業務的需求迅猛增加，促進了寬帶無線通信新技術的發展和應用。正交頻分復用 (Orthogonal Frequency Division Multiolexing，OFDM)技術已經廣泛應用于各種高速寬帶無線通信系統中。然而 OFDM 系統相比單載波系統更容易受到頻偏和時偏的影響，因此如何有效地消除頻偏和時偏，實現系統的時頻同步是 OFDM 系統中非常關鍵的技術。本文討論了非同步對 OFDM 系統的影響，分析了當前用于 OFDM 系統中基于數據符號的同步算法，并簡單介紹非基于數據符號同步技術。基于數據符號的同步技術通過加入訓練符號或導頻等附加信息，并利用導頻或訓練符號的相關性實現時頻同步。此算法由于加入了附加信息，降低了帶寬利用率，但同步精度相對較高，同步捕獲時間較短。隨著電子芯片技術的快速發展，電子設計自動化 (Electronic DesignAutomation，EDA) 技術和可編程邏輯芯片 (FPGA/CPLD) 的應用越來越受到大家的重視，為此文中對 EDA 技術和 Altera 公司制造的 FPGA 芯片的原理和結構特點進行了闡述，還介紹了在相關軟件平臺進行開發的系統流程。論文在對基于數據符號三種算法進行較詳細的分析和研究的基礎上，尤其改進了基于導頻符號的同步算法之后，利用 Altera 公司的 FPGA 芯片EP1S25F102015 在 OuartusⅡ5.0 工具平臺上實現了 OFDM 同步的硬件設計，然后進行了軟件仿真。其中對基于導頻符號同步的改進算法硬件設計過程了進行了詳細闡述。不僅如此，對于基于 PN 序列幀的同步算法和基于循環前綴 (Cycle Prefix，CP) 的極大似然 (Maximam Likelihood,ML)估計同步算法也有具體的仿真實現。最后，文章還對它們進行了比較，基于導頻符號同步設計的同步精度比較高，但是耗費芯片的資源多，另一個缺點是沒有頻偏估計，因此運用受到一定限制。基于 PN 序列幀的同步設計使用了最少的芯片資源，但要提取 PN 序列中的信號數據有一定困難。基于循環前綴的同步設計占用了芯片 I/O 腳稍顯多。這幾種同步算法各有優缺點，但可以根據不同的信道環境選用它們。

標簽： FPGA 數據同步的仿真實現

上傳時間： 2013-04-24

上傳用戶：斷點PPpp
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise

This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal Likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.

標簽： instantaneous algorithm Bayesian Gaussian

上傳時間： 2013-12-19

上傳用戶：jjj0202

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Likelihood

LDPC碼譯碼相關文獻 Bounds on the maximum Likelihood decoding error probability of low density parity check

％ This routine provides a convenient way to produce Pd/FAD information ％ from Likelihood ratio info

A general decision rule for stochastic blind maximum-Likelihood OSTBC detection is derived.

A stack-based sequential depth-first decoder that returns Maximum-Likelihood solutions to spherical

he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum Likelihood form

Maximum Likelihood Methods in Radar Array Signal Processing

This demo shows the BER performance of linear, decision feedback (DFE), and maximum Likelihood seque

Implements Maximum Likelihood estimation of beta and other parameters for model of stock portfolio v

基于數據符號同步的FPGA仿真實現

This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise