本文選題：衛(wèi)星信道　切入點：盲均衡　出處：《南京信息工程大學》2016年碩士論文

【摘要】：在數(shù)字通信系統(tǒng)中不可避免地存在非線性效應(yīng),這種非線性將產(chǎn)生嚴重的幅度畸變和碼間干擾,對通信系統(tǒng)數(shù)據(jù)傳輸速率的提高是一個阻礙,因此在接收端需要通過非線性均衡以克服碼間干擾和幅度畸變。這種非線性效應(yīng)的產(chǎn)生主要來源于衛(wèi)星內(nèi)部放大器的非線性特性,本論文的主要內(nèi)容是對非線性衛(wèi)星信道均衡做了一個系統(tǒng)的研究,針對非線性衛(wèi)星信道產(chǎn)生的干擾和畸變,在傳統(tǒng)盲均衡算法的基礎(chǔ)上,結(jié)合Volterra級數(shù),MMSE,Turbo,模糊神經(jīng)網(wǎng)絡(luò),復數(shù)神經(jīng)網(wǎng)絡(luò)等一系列方法,提出了多種基于非線性Volterra衛(wèi)星信道的盲均衡算法,并通過理論分析和計算機仿真實驗證明所提算法的有效性。具體研究內(nèi)容如下：1.針對衛(wèi)星信道的非線性,用Volterra模型模擬非線性衛(wèi)星信道,利用逆濾波原理并結(jié)合盲均衡算法分析了傳統(tǒng)Volterra均衡器并研究了改進的Volterra均衡器。理論分析和仿真結(jié)果表明,改進的Volterra均衡器的運算量有所減小。2.非線性產(chǎn)生的碼間干擾是影響衛(wèi)星信道通信的重要因素之一,運用Volterra級數(shù)分解來表示非線性信道,為了能夠同時消除線性和非線性干擾,推導了基于MMSE的Turbo均衡算法以及無先驗信息和低復雜度的兩種近似算法；而且為了提高帶寬利用率,引入盲均衡算法,提出了基于線性MMSE的迭代Turbo盲均衡算法。仿真結(jié)果表明,基于MMSE的迭代Turbo盲均衡算法誤碼性能有明顯提高。3.針對傳統(tǒng)常模算法收斂速度與剩余均方誤差之間的矛盾及傳統(tǒng)神經(jīng)網(wǎng)絡(luò)參數(shù)太多、復雜度高的問題,提出了基于模糊神經(jīng)網(wǎng)絡(luò)控制的復數(shù)神經(jīng)多項式常模盲均衡算法。該算法中的復數(shù)神經(jīng)多項式模塊包含單層神經(jīng)網(wǎng)絡(luò)和非線性處理器,結(jié)構(gòu)簡單,比傳統(tǒng)的多層神經(jīng)網(wǎng)絡(luò)或遞歸神經(jīng)網(wǎng)絡(luò)參數(shù)少復雜度低；而且,利用模糊神經(jīng)網(wǎng)絡(luò)模塊設(shè)計的模糊規(guī)則控制迭代步長,提高了步長控制的精度。理論分析和仿真結(jié)果表明,該算法具有簡單的系統(tǒng)結(jié)構(gòu)、較快的收斂速度和較小的穩(wěn)態(tài)誤差,較好的解決了因為參數(shù)多而造成的高復雜度問題,而且克服了收斂速度與均方誤差之間的矛盾。
[Abstract]:The nonlinear effect inevitably exists in the digital communication system, which will produce serious amplitude distortion and inter-symbol interference, which is a hindrance to the improvement of the data transmission rate of the communication system.Therefore, nonlinear equalization is needed at the receiver to overcome inter-symbol interference and amplitude distortion.This nonlinear effect mainly comes from the nonlinear characteristics of the satellite internal amplifier. The main content of this paper is to do a systematic study on the nonlinear satellite channel equalization, aiming at the interference and distortion caused by the nonlinear satellite channel.On the basis of the traditional blind equalization algorithm and a series of methods such as Volterra series MMSE Turbo, fuzzy neural network and complex neural network, several blind equalization algorithms based on nonlinear Volterra satellite channel are proposed.The effectiveness of the proposed algorithm is proved by theoretical analysis and computer simulation.The specific contents of the study are as follows: 1.Aiming at the nonlinearity of satellite channel, the nonlinear satellite channel is simulated by Volterra model. The traditional Volterra equalizer is analyzed by using the inverse filtering principle and the blind equalization algorithm, and the improved Volterra equalizer is studied.Theoretical analysis and simulation results show that the computational complexity of the improved Volterra equalizer is reduced by .2.The nonlinear inter-symbol interference (ISI) is one of the most important factors affecting satellite channel communication. The Volterra series decomposition is used to represent the nonlinear channel in order to eliminate both linear and nonlinear interference at the same time.The Turbo equalization algorithm based on MMSE and two approximate algorithms without prior information and low complexity are derived, and in order to improve the bandwidth utilization, a blind equalization algorithm is introduced, and an iterative Turbo blind equalization algorithm based on linear MMSE is proposed.Simulation results show that the BER performance of iterative Turbo blind equalization algorithm based on MMSE is significantly improved.Aiming at the contradiction between convergence speed and residual mean square error of traditional constant norm algorithm and the problem that the traditional neural network has too many parameters and high complexity, a complex neural polynomial constant modulus blind equalization algorithm based on fuzzy neural network control is proposed.The complex neural polynomial module in this algorithm consists of a single layer neural network and a nonlinear processor, which has a simple structure and less complexity than the traditional multilayer neural network or recurrent neural network.The iterative step size of fuzzy rule control is designed by using fuzzy neural network module, and the precision of step size control is improved.Theoretical analysis and simulation results show that the algorithm has simple system structure, faster convergence speed and smaller steady-state error, and solves the problem of high complexity caused by many parameters.Moreover, the contradiction between convergence rate and mean square error is overcome.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN911.5;TN927.2
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本文編號：1707491