發(fā)布時(shí)間：2018-03-11 04:10

  本文選題：盲均衡　切入點(diǎn)：優(yōu)化理論　出處：《解放軍信息工程大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：近年來,工業(yè)化的快速發(fā)展使得開放的電磁環(huán)境更加復(fù)雜.,在無線通信中由信道特性不理想所引起的碼間干擾更加嚴(yán)重,對信號(hào)的接收與恢復(fù)帶來了新的挑戰(zhàn),也對消除碼間干擾的盲均衡器的性能提出了更高要求。.基于此,本課題以提高盲均衡器的性能為目的,利用現(xiàn)代優(yōu)化方法,如線性規(guī)劃,半正定規(guī)劃,向量范數(shù)等理論,研究了提高盲均衡器收斂速度的方法,設(shè)計(jì)了更能反映信號(hào)統(tǒng)計(jì)特征的代價(jià)函數(shù)以降低穩(wěn)態(tài)誤差,構(gòu)造了凸代價(jià)函數(shù)以保證均衡器收斂性。主要工作有以下幾方面：1、研究提高盲均衡器收斂速度的方法。研究改進(jìn)了基于隨機(jī)梯度下降法尋找代價(jià)函數(shù)最優(yōu)值的方式,針對多模盲均衡算法給出了一種新的最速下降優(yōu)化實(shí)現(xiàn)方法,新方法通過將信道輸出統(tǒng)計(jì)量的估計(jì)與均衡器迭代相分離,使得均衡器在每次迭代后不需要對其輸出統(tǒng)計(jì)量重新計(jì)算,避免了對信道輸出數(shù)據(jù)循環(huán)重用所導(dǎo)致的較長處理時(shí)延,適合并行化實(shí)時(shí)處理。新的迭代方法具有更快的收斂速度和更小的穩(wěn)態(tài)誤差,只要總的處理時(shí)間不超過數(shù)據(jù)緩存時(shí)間,就可以實(shí)現(xiàn)流水線式的實(shí)時(shí)處理,尤其適合數(shù)據(jù)包傳輸場合。2、針對Bussgang類盲均衡算法大都基于非凸代價(jià)函數(shù),對其優(yōu)化往往會(huì)陷入局部極值點(diǎn)因而不能充分消除碼間干擾的問題。本文給出了一種針對QAM信號(hào)盲均衡的凸代價(jià)函數(shù),并將凸優(yōu)化求解問題轉(zhuǎn)化為一種可快速計(jì)算的線性規(guī)劃,新的代價(jià)函數(shù)在恒定約束下,具有較少的未知變量和約束方程,復(fù)雜度較低。為克服恒定約束條件下,對某些信道的均衡可能存在收斂速度慢,甚至不收斂的問題,本文又給出一種基于自適應(yīng)約束凸代價(jià)函數(shù)的盲均衡算法,該算法只需要很少的信號(hào)樣點(diǎn),經(jīng)過多次迭代即可達(dá)到更好的收斂性能,可適用于短時(shí)信號(hào)的盲均衡。相比于傳統(tǒng)的MMA算法,新方法采用了凸代價(jià)函數(shù),能夠保證均衡器收斂至理想值。3、針對Bussgang類算法代價(jià)函數(shù)對信號(hào)所包含信息未充分利用,致使穩(wěn)態(tài)誤差大的問題,本文研究并設(shè)計(jì)了更能反映信號(hào)統(tǒng)計(jì)特征的代價(jià)函數(shù),以降低穩(wěn)態(tài)誤差,依據(jù)向量范數(shù)性質(zhì),結(jié)合將約束方程轉(zhuǎn)換為無約束方程的方法,給出了一種新的無約束盲均衡準(zhǔn)則,證明了無噪聲情況下,代價(jià)函數(shù)的局部最優(yōu)解即對應(yīng)理想均衡條件,且能夠?qū)崿F(xiàn)發(fā)送信號(hào)幅值的恢復(fù)�？刹捎门鷶�(shù)據(jù)處理和在線自適應(yīng)迭代兩種方法更新均衡器系數(shù),無需自動(dòng)增益控制,算法實(shí)現(xiàn)簡單,同時(shí)穩(wěn)態(tài)誤差更小。4、針對傳統(tǒng)的峰度最大化盲均衡算法不能糾正均衡輸出的相位偏移,以及功率最大化算法在低信噪比下性能下降較大的問題。本文利用向量范數(shù)性質(zhì),基于均衡器輸出峰度最大化的思想,’給出了一種對加性高斯噪聲更加魯棒的方形QAM信號(hào)盲均衡與載波相位恢復(fù)準(zhǔn)則,證明了無噪聲情況下,代價(jià)函數(shù)的局部最優(yōu)解即對應(yīng)理想均衡條件,并采用批數(shù)據(jù)處理的方法求均衡器權(quán)值。相比于傳統(tǒng)的Donoho算法、CMA、SW、SFA等峰度最大化算法,新算法能夠糾正均衡輸出的相位偏移,相比功率最大化的QP-FSE算法以及自適應(yīng)的迭代求解算法βMMA,新算法受加性高斯噪聲的影響更小。5、針對盲均衡中對非凸代價(jià)函數(shù)的優(yōu)化存在收斂速度慢,對初始化條件和步長敏感,且容易誤收斂的問題,本文采用半正定規(guī)劃方法對傳統(tǒng)的采用隨機(jī)梯度下降法實(shí)現(xiàn)的盲均衡算法進(jìn)行改進(jìn),通過對MSOSA算法代價(jià)函數(shù)的修正,給出了一種新的基于半正定規(guī)劃的求解算法,并將其松弛為凸規(guī)劃,相比采用隨機(jī)梯度下降法的求解方法,新算法需要的數(shù)據(jù)樣點(diǎn)更少,且收斂后的穩(wěn)態(tài)誤差更小,該方法尤其適合于數(shù)據(jù)量獲取較少的場合。
[Abstract]:In recent years, the rapid development of industrialization makes the electromagnetic environment open more complicated. In wireless communication, the non ideal channel characteristics caused by intersymbol interference is more serious, the signal is received and the recovery has brought new challenges, but also put forward higher requirements. Based on the performance of the blind equalizer to eliminate intersymbol interference in this paper, in order to improve the performance of blind equalizer for the purpose of using modern optimization methods, such as linear programming, semidefinite programming, vector norm theory, research methods to improve the convergence rate of blind equalizer, a cost function is designed to better reflect the statistical characteristics of the signal in order to reduce the steady-state error, the convex cost function to construct ensure the equalizer convergence. The main works are as follows: 1. Research on the method of improving the convergence rate of blind equalizer. The improved cost function for the optimal value of the stochastic gradient descent method based on the way, According to the multi modulus blind equalization algorithm is given a new steepest descent optimization method, a new method by the iterative channel estimation and equalizer output statistics of phase separation, the equalizer after each iteration does not need to recalculate the output statistics, avoid long processing delay caused by the channel output cycle of data reuse, suitable for parallel real-time processing. The steady-state error of new iterative method has faster convergence speed and smaller, as long as the total processing time is less than the data cache time, can achieve real-time processing pipeline, especially suitable for packet transmission occasions.2, for the Bussgang blind equalization algorithms are based on non convex cost function for optimization tend to fall into local extremum and cannot fully eliminate the ISI problem. This paper presents a blind equalization for QAM signals with convex cost function, And the convex optimization problem is transformed into a fast calculation of linear programming, a new cost function under constant constraints, variables and constraint equations with fewer and lower complexity. In order to overcome the constant constraint conditions of some channel equilibrium may exist slow convergence, even no convergence problem in this paper, and presents an adaptive blind equalization algorithm based on constrained convex cost function, the algorithm requires only a few signals, after several iterations to achieve better convergence performance, blind equalization can be applied to the short-time signal. Compared with the traditional MMA algorithm, a new method using a convex cost function, can guarantee the equalizer converge to the ideal value of.3, according to the Bussgang algorithm cost function does not make full use of the information contained in the signal, resulting in the problem of steady-state error, this paper study and design to better reflect the statistical characteristics of the signal The cost function, in order to reduce the steady-state error, based on the vector norm properties, combining constraint equations into unconstrained equations, a new unconstrained blind equalization criterion is given, that the absence of noise, the local optimal solution of the cost function corresponding to the ideal equilibrium conditions, which can realize the amplitude of the transmitted signal the number of recovery. Data processing and online adaptive iterative methods to update two equalizer coefficients, without the automatic gain control algorithm is simple, and smaller steady-state error.4, the phase shift of traditional kurtosis maximization blind equalization algorithm can not correct the equilibrium output, and the maximum power algorithm in the low SNR performance decline the larger problem. Using the properties of vector norm, the equalizer output kurtosis maximization based on the idea of "gives an additive Gauss noise robust square QAM signal Blind equalization and carrier phase recovery, proved the absence of noise, the local optimal solution of the cost function corresponding to the ideal equilibrium condition, and the method of batch data processing for the equalizer weights. Compared with the traditional Donoho algorithm, CMA, SW, SFA, kurtosis maximization algorithm, the new algorithm can correct the phase equilibrium output the maximum power compared to the QP-FSE algorithm and adaptive iterative algorithm of beta MMA, the new algorithm by smaller.5 effects of additive Gauss noise, aiming at the optimization of non convex cost function blind equalization in slow convergence for the initial condition and the step of sensitive and easy convergence problem, this paper adopts semi definite the planning of the traditional method of using stochastic gradient descent method to achieve blind equalization algorithm is improved by modifying the cost function of MSOSA algorithm, a new positive semi definite programming requirements are given based on The algorithm is relaxed to convex programming. Compared with the method of stochastic gradient descent algorithm, the new algorithm needs less data points and smaller steady-state error after convergence. This method is especially suitable for less data acquisition.

【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.5

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