發(fā)布時間：2018-03-02 19:39

  本文選題：MIMO　切入點：LDPC-BICM-ID系統(tǒng)　出處：《鄭州大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：比特交織編碼調(diào)制(Bit-interleaved Coded Modulation，BICM)可以有效提高頻譜利用率，LDPC(Low-density-parity-check)碼具有低延時并行的強糾錯性能，，LDPC-BICM充分結(jié)合兩者的優(yōu)勢，可以獲得良好的編碼增益，是未來寬帶移動通信系統(tǒng)中的關(guān)鍵技術(shù)之一。但在信道信息未知或多徑信道下，如何精確估計信道和消除符號間干擾，并利用迭代結(jié)構(gòu)交換軟解調(diào)和譯碼信息，以盡可能低的計算成本獲得更佳的迭代性能仍是LDPC-BICM-ID(LDPC-BICM-Iterative decoding)系統(tǒng)需要解決的問題。 本文以概率圖模型(Probability Graphical Model, PGM)為工具，研究了在不同信道和噪聲環(huán)境下LDPC-BICM-ID系統(tǒng)的迭代譯碼問題、聯(lián)合信道估計與譯碼問題、聯(lián)合檢測與譯碼問題，論文的主要工作和創(chuàng)新點可以概括為以下幾個方面： 1.提出了一種基于概率圖模型的低復(fù)雜度自適應(yīng)置信差分LDPC譯碼算法。通過展開節(jié)點的圖變換方法降低校驗節(jié)點消息的計算復(fù)雜度。同時，為彌補由復(fù)雜度降低而造成的性能損失，一方面自適應(yīng)地調(diào)整校驗節(jié)點消息的歸一化系數(shù)，另一方面僅當(dāng)變量節(jié)點的消息值振蕩時引入差分映射策略，給出了一種選擇性的置信差分規(guī)則，該規(guī)則有效地加快了收斂速度，減少了實際總迭代次數(shù)。仿真結(jié)果表明，和對數(shù)似然的置信傳播算法相比，所提出的譯碼算法降低了在低信噪比區(qū)域的計算復(fù)雜度，提高了在高信噪比區(qū)域的性能。 2.研究LDPC-BICM-ID系統(tǒng)在最優(yōu)映射下無迭代增益問題，借鑒LDPC圖模型中的重加權(quán)方法，創(chuàng)建新的迭代結(jié)構(gòu)，提出了均勻重加權(quán)迭代譯碼算法和重加權(quán)差分映射的最小和迭代接收算法。不同于傳統(tǒng)結(jié)構(gòu)，本文在反饋部分添加調(diào)制器和加權(quán)值乘法器，在譯碼器中引入指數(shù)型加權(quán)先驗信息，用聯(lián)合概率信息取代外信息，使解調(diào)器輸出的互信息值在迭代中不斷增加，獲得最優(yōu)映射下的迭代增益。給出了互信息理論證明，討論了加權(quán)值對性能的影響。不僅運用變分理論給出了定性分析，而且利用外信息轉(zhuǎn)移特性圖預(yù)測了最優(yōu)取值。仿真結(jié)果表明在高斯和瑞利衰落信道下，上述兩種算法均獲得迭代增益。 3.針對時間選擇性平坦衰落信道的信息未知時，LDPC-BICM-ID系統(tǒng)中參數(shù)消息傳遞的信道估計方法穩(wěn)健性差，已有的非消息傳遞迭代接收算法復(fù)雜度高等問題，建立了系統(tǒng)的概率圖模型，提出了一種融合MCMC(Markov chain MonteCarlo，MCMC)粒子濾波的非參數(shù)消息傳遞算法。為提高信道估計精度的同時降低消息更新的復(fù)雜度，推導(dǎo)了MCMC最大和(max-sum)消息更新規(guī)則，與解調(diào)/譯碼模塊的消息更新規(guī)則統(tǒng)一為最大和形式。在此基礎(chǔ)上，從局部考慮設(shè)計了低復(fù)雜的粒子集消息調(diào)度機制，先后利用粒子集與粒子集眾數(shù)計算信道估計部分的外信息，給出了選擇性更新機制以減少譯碼與解調(diào)之間交換的外信息數(shù)量。同時從全局出發(fā)設(shè)計了稀疏性信息調(diào)度機制從而降低復(fù)雜度，同時為信道估計提供了更準(zhǔn)確的譯碼外信息，進一步提高了估計性能。仿真結(jié)果表明，該聯(lián)合信道估計與譯碼算法提高性能的同時有效降低了計算復(fù)雜度，而且穩(wěn)健性強。 4.研究IS（Iinter-symbol-interference）信道下LDPC編碼的SISO（single-Inputsingle-Output）/MIMO（Multiple-Input Multiple-Output）系統(tǒng)中聯(lián)合迭代檢測與譯碼問題。首先，依據(jù)馬爾科夫隨機場理論提出了基于邊出現(xiàn)概率和基于因子出現(xiàn)概率的重加權(quán)算法，提高信道均衡即檢測性能。然后，將上述檢測算法與LDPC譯碼算法統(tǒng)一在均勻重加權(quán)消息更新方法的框架下，設(shè)計了合理的全局和判決式調(diào)度機制，提出了基于概率圖模型的聯(lián)合迭代檢測與譯碼算法。仿真結(jié)果表明，上述算法提高性能的同時降低了迭代成本。
[Abstract]:Bit interleaved encoding modulation (Bit-interleaved Coded Modulation, BICM) can effectively improve the spectrum utilization, LDPC (Low-density-parity-check) code has strong error correcting performance and low latency in parallel, LDPC-BICM combines the advantages of both, can obtain good encoding gain, is one of the key technologies in future broadband mobile communication system. But the channel information is unknown or the multipath channel, how to accurately estimate the channel and intersymbol interference, and the iterative structure exchange soft demodulation and decoding information, to calculate the cost as low as possible to obtain better performance of iteration is still LDPC-BICM-ID (LDPC-BICM-Iterative decoding) system needs to solve the problem.
In this paper, the probabilistic graphical model (Probability Graphical Model, PGM) as a tool to study the problem in the iterative decoding of LDPC-BICM-ID system with different channel and noise environment, joint channel estimation and decoding, joint detection and decoding problem, the main work and innovation points can be summarized as follows:
1. propose a low complexity adaptive probability graph model of the messenger LDPC decoding algorithm based on graph transformation method. By expanding node to reduce the computational complexity of the check node message. At the same time, the performance loss caused by compensate for reduced complexity, a normalized coefficient adaptively adjusts the check node message, difference divided into mapping strategy on the other hand only when the variable node message value oscillation, with the confidence of a selective differential rule, this rule effectively accelerates the convergence rate, reduce the total number of iterations. The simulation result shows that the belief propagation algorithm and log likelihood decoding algorithm compared to the proposed reduction in the low SNR region of computational complexity, improves in the high SNR region performance.
Study on 2. LDPC-BICM-ID system without iterative gain in the optimal mapping problem, from the heavy weighting method LDPC graph model, creating a new iterative structure, puts forward the uniform weight weighted iterative decoding algorithm and weighted difference maps and minimum iterative receiver algorithm. Different from the traditional structure, this paper added in feedback modulator and weighted the value weighted index multiplier, introducing a priori information in the decoder, to replace the information by joint probability information, the mutual information of the demodulator output value increased in the iteration, obtain the iterative gain optimal mapping are proved. The mutual information theory, discusses the influence on the performance of the weighted value. Not only the use of variational theory give a qualitative analysis, and predict the optimal value of using extrinsic information transfer characteristics. The simulation results show that in Gauss and Rayleigh fading channel, the above two algorithms are obtained iteratively Gain.
For the 3. time selective flat fading channel information is unknown channel parameters in the LDPC-BICM-ID system message estimation method has poor robustness, non news transmission iterative receiver algorithm complexity is high, a probability graph model of the system, put forward a kind of fusion of MCMC (Markov chain MonteCarlo, MCMC) non parametric particle filtering message the transfer algorithm. In order to improve the channel estimation accuracy and reduce the complexity of update, derived MCMC (max-sum) and the maximum message update rules, and demodulation / decoding module of the message update rules for the largest and unified form. On this basis, from partial consideration to design low complex particle set message scheduling mechanism. Has the use of particle sets and particle set mode calculation of channel estimation information part, selective update mechanism to reduce information exchange between decoding and demodulation is given At the same time. The number of starting from the overall design of the sparse information scheduling mechanism in order to reduce the complexity, and provides more accurate information for decoding channel estimation, to further improve the estimation performance. The simulation results show that the joint channel estimation and decoding algorithm to improve performance and effectively reduce the computational complexity, and robustness.
4. of IS (Iinter-symbol-interference) LDPC encoding channel SISO (single-Inputsingle-Output) /MIMO (Multiple-Input Multiple-Output) joint iterative detection and decoding system. Firstly, based on the Markov random field theory, probability and probability of edge factor weight weighted algorithm based on channel equalization is to improve detection performance. Then, the detection algorithm and LDPC decoding algorithm in the framework of unified uniform reweighted message updating method, and designs a global decision scheduling mechanism is reasonable, the joint iterative detection and decoding probability graph model based algorithm. Simulation results show that at the same time improve the performance of the algorithm reduces the iteration cost.

【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN911.22

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