LDPC碼譯碼相關(guān)文獻(xiàn) Bounds on the maximum likelihood decoding error probability of low density parity check codes
標(biāo)簽： probability likelihood decoding maximum
上傳時(shí)間： 2015-11-25
上傳用戶(hù)：wendy15
％ This routine provides a convenient way to produce Pd/FAD information ％ from likelihood ratio information.
標(biāo)簽： information convenient likelihood provides
上傳時(shí)間： 2016-01-05
上傳用戶(hù)：liglechongchong
A general decision rule for stochastic blind maximum-likelihood OSTBC detection is derived.
標(biāo)簽： maximum-likelihood stochastic detection decision
上傳時(shí)間： 2013-12-04
上傳用戶(hù)：xcy122677
A stack-based sequential depth-first decoder that returns Maximum-likelihood solutions to spherical LAST coded MIMO system-type problems
標(biāo)簽： Maximum-likelihood stack-based depth-first sequential
上傳時(shí)間： 2013-12-20
上傳用戶(hù)：hebmuljb
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(biāo)簽： equivalent likelihood algorithm Sejnowski
上傳時(shí)間： 2016-09-17
上傳用戶(hù)：Altman
Maximum likelihood Methods in Radar Array Signal Processing
標(biāo)簽： likelihood Processing Maximum Methods
上傳時(shí)間： 2013-12-29
上傳用戶(hù)：dave520l
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(biāo)簽： performance likelihood decision feedback
上傳時(shí)間： 2013-11-25
上傳用戶(hù)：1079836864
Implements Maximum likelihood estimation of beta and other parameters for model of stock portfolio vs. index using kalman filter
標(biāo)簽： Implements likelihood estimation parameters
上傳時(shí)間： 2013-12-28
上傳用戶(hù)：zhangzhenyu
近年來(lái)，人們對(duì)無(wú)線數(shù)據(jù)和多媒體業(yè)務(wù)的需求迅猛增加，促進(jìn)了寬帶無(wú)線通信新技術(shù)的發(fā)展和應(yīng)用。正交頻分復(fù)用 (Orthogonal Frequency Division Multiolexing，OFDM)技術(shù)已經(jīng)廣泛應(yīng)用于各種高速寬帶無(wú)線通信系統(tǒng)中。然而 OFDM 系統(tǒng)相比單載波系統(tǒng)更容易受到頻偏和時(shí)偏的影響，因此如何有效地消除頻偏和時(shí)偏，實(shí)現(xiàn)系統(tǒng)的時(shí)頻同步是 OFDM 系統(tǒng)中非常關(guān)鍵的技術(shù)。 本文討論了非同步對(duì) OFDM 系統(tǒng)的影響，分析了當(dāng)前用于 OFDM 系統(tǒng)中基于數(shù)據(jù)符號(hào)的同步算法，并簡(jiǎn)單介紹非基于數(shù)據(jù)符號(hào)同步技術(shù)。基于數(shù)據(jù)符號(hào)的同步技術(shù)通過(guò)加入訓(xùn)練符號(hào)或?qū)ьl等附加信息，并利用導(dǎo)頻或訓(xùn)練符號(hào)的相關(guān)性實(shí)現(xiàn)時(shí)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(duì)較高，同步捕獲時(shí)間較短。 隨著電子芯片技術(shù)的快速發(fā)展，電子設(shè)計(jì)自動(dòng)化 (Electronic DesignAutomation，EDA) 技術(shù)和可編程邏輯芯片 (FPGA/CPLD) 的應(yīng)用越來(lái)越受到大家的重視，為此文中對(duì) EDA 技術(shù)和 Altera 公司制造的 FPGA 芯片的原理和結(jié)構(gòu)特點(diǎn)進(jìn)行了闡述，還介紹了在相關(guān)軟件平臺(tái)進(jìn)行開(kāi)發(fā)的系統(tǒng)流程。 論文在對(duì)基于數(shù)據(jù)符號(hào)三種算法進(jìn)行較詳細(xì)的分析和研究的基礎(chǔ)上，尤其改進(jìn)了基于導(dǎo)頻符號(hào)的同步算法之后，利用 Altera 公司的 FPGA 芯片EP1S25F102015 在 OuartusⅡ5.0 工具平臺(tái)上實(shí)現(xiàn)了 OFDM 同步的硬件設(shè)計(jì)，然后進(jìn)行了軟件仿真。其中對(duì)基于導(dǎo)頻符號(hào)同步的改進(jìn)算法硬件設(shè)計(jì)過(guò)程了進(jìn)行了詳細(xì)闡述。不僅如此，對(duì)于基于 PN 序列幀的同步算法和基于循環(huán)前綴 (Cycle Prefix，CP) 的極大似然 (Maximam likelihood,ML)估計(jì)同步算法也有具體的仿真實(shí)現(xiàn)。 最后，文章還對(duì)它們進(jìn)行了比較，基于導(dǎo)頻符號(hào)同步設(shè)計(jì)的同步精度比較高，但是耗費(fèi)芯片的資源多，另一個(gè)缺點(diǎn)是沒(méi)有頻偏估計(jì)，因此運(yùn)用受到一定限制。基于 PN 序列幀的同步設(shè)計(jì)使用了最少的芯片資源，但要提取 PN 序列中的信號(hào)數(shù)據(jù)有一定困難。基于循環(huán)前綴的同步設(shè)計(jì)占用了芯片 I/O 腳稍顯多。這幾種同步算法各有優(yōu)缺點(diǎn)，但可以根據(jù)不同的信道環(huán)境選用它們。
標(biāo)簽： FPGA 數(shù)據(jù) 同步的 仿真實(shí)現(xiàn)
上傳時(shí)間： 2013-04-24
上傳用戶(hù)：斷點(diǎn)PPpp
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(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時(shí)間： 2013-12-19
上傳用戶(hù)：jjj0202
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