本文選題：水聲信道估計 + 正交頻分復(fù)用��； 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：水聲信道具有時-空-頻變性、帶寬有限、多徑干擾嚴重,是迄今為止最復(fù)雜的無線信道。正交頻分復(fù)用(OFDM)技術(shù)特別適合于帶寬有限的水聲信道,可有效對抗多徑造成的碼間干擾。隨著通信距離的增加,信道狀態(tài)不斷惡化,造成接收信號在幅度、相位和頻率上的失真。所以需要利用信道估計技術(shù)對信道狀態(tài)進行跟蹤,并對接收到的信號進行補償。本文主要研究基于OFDM系統(tǒng)的水聲信道估計技術(shù)。在傳統(tǒng)的水聲信道估計中,均假設(shè)是豐富多徑信道,使用奈奎斯特采樣定理對信道沖擊響應(yīng)進行采樣,需要插入大量的導(dǎo)頻進行信道估計,降低系統(tǒng)信息傳輸速率和頻譜利用率。壓縮感知理論(Compressive Sensing,CS)打破了傳統(tǒng)的奈奎斯特采樣定理,在采樣的同時對信號進行壓縮,使用少量的采樣值就能高概率的重構(gòu)出原始信號。使用標準CS理論進行估計是基于稀疏水聲信道模型。然而大量海洋實驗表明:由于海底介質(zhì)的非均勻性,導(dǎo)致聲線以塊的形式進行傳播,水聲信道呈現(xiàn)出塊結(jié)構(gòu)稀疏特性。由于水聲信道具有內(nèi)在塊結(jié)構(gòu)稀疏特性,因此可以將信道估計問題轉(zhuǎn)化為塊結(jié)構(gòu)稀疏信號重構(gòu)問題,使用結(jié)構(gòu)化壓縮感知理論對信道進行塊稀疏采樣和重構(gòu)。大幅降低了信道重構(gòu)所需插入的導(dǎo)頻數(shù)量,提高了系統(tǒng)頻帶利用率,且避免了對無用零抽頭的估計,提高了重構(gòu)的效率。本文首先介紹水聲信道自身的物理特性和OFDM系統(tǒng)的基本通信原理,在射線理論的基礎(chǔ)上構(gòu)建水聲相干多徑信道模型,簡單介紹了傳統(tǒng)的最小二乘(Least Square,LS)信道估計方法。接著重點介紹壓縮感知理論,在此基礎(chǔ)上描述了塊結(jié)構(gòu)稀疏信號的概念及其相關(guān)的重構(gòu)算法,重點介紹了經(jīng)典的塊正交匹配追蹤(Block Orthogonal Matching Pursuit,BOMP)算法。引入多項正交匹配思想,將改進的BOMP算法應(yīng)用于水聲信道估計中。基于導(dǎo)頻的信道估計方法計算簡單,易于實現(xiàn),得到了廣泛的應(yīng)用。本文使用基于導(dǎo)頻的信道估計方法,將傳統(tǒng)的LS,基于稀疏模型的正交匹配追蹤(Orthogonal Matching Pursuit,OMP),基于塊稀疏模型的BOMP及其改進的算法都應(yīng)用到水聲中,進行水聲信道的重構(gòu)。本文對水聲信道估計進行系統(tǒng)的實驗仿真。仿真結(jié)果表明:基于塊結(jié)構(gòu)稀疏模型的BOMP算法估計性能要優(yōu)于傳統(tǒng)的LS和OMP算法,插入少量的導(dǎo)頻就能獲得更優(yōu)的估計性能,同時降低信道重構(gòu)所需的時間;改進的BOMP算法在保證和BOMP算法重構(gòu)精度一致的基礎(chǔ)上,進一步降低重構(gòu)所需的時間。
[Abstract]:Underwater acoustic signal props are sometimes space - frequency denaturation, limited bandwidth and serious multipath interference. It is the most complex wireless channel so far. Orthogonal frequency division multiplexing (OFDM) technology is especially suitable for the underwater acoustic channel with limited bandwidth. It can effectively combat intersymbol interference caused by multipath. With the increase of communication distance, the channel state is deteriorating, resulting in receiving signal in the channel. Distortion in amplitude, phase and frequency. Therefore, channel estimation techniques need to be used to track the channel state and compensate the received signals. This paper mainly studies the underwater acoustic channel estimation based on OFDM system. In the traditional underwater acoustic channel estimation, it is assumed that the multipath channel is rich rich, and the Nyquist sampling theorem is used for the letter. Compressive Sensing (CS) breaks the traditional Nyquist sampling theorem, compresses the signal at the same time of sampling, and uses a small amount of sampling values to reconstruct high probability. The original signal. The standard CS theory is based on the sparse underwater acoustic channel model. However, a large number of ocean experiments show that the acoustic line is propagated in block form due to the heterogeneity of the submarine medium, and the underwater acoustic channel presents a sparsity of block structure. The channel estimation problem is transformed into a block structure sparse signal reconstruction problem. The structured compressed sensing theory is used for the sparse sampling and reconstruction of the channel block. The number of pilots needed to be inserted in the channel reconstruction is greatly reduced, the utilization rate of the system is improved, and the estimation of the useless zero taps is avoided, and the efficiency of the reconstruction is improved. This paper first introduces the efficiency of the reconstruction. The physical characteristics of Shaoxing sound channel and the basic communication principle of OFDM system are used to construct the multipath channel model of underwater acoustic coherence on the basis of ray theory. The traditional Least Square (LS) channel estimation method is briefly introduced. Then the compression perception theory is introduced. On this basis, the concept of block structure sparse signal is described. And related reconstruction algorithms, the classic block orthogonal matching tracking (Block Orthogonal Matching Pursuit, BOMP) algorithm is introduced. The improved BOMP algorithm is applied to the underwater acoustic channel estimation by introducing a number of orthogonal matching ideas. The channel estimation method based on pilot is simple and easy to implement, and has been widely used. This paper makes a wide application. Using the channel estimation method based on pilot, the traditional LS, Orthogonal Matching Pursuit (OMP) based on sparse model, BOMP based on block sparse model and its improved algorithm are applied to underwater acoustic channel reconstruction. The experimental simulation of underwater acoustic channel estimation is carried out in this paper. The simulation results show that: The estimation performance of BOMP algorithm based on block structure sparse model is better than the traditional LS and OMP algorithm. Inserting a small number of pilots can obtain better estimation performance and reduce the time needed for channel reconstruction. The improved BOMP algorithm further reduces the time needed for reconstruction on the basis of ensuring the consistency of the reconstruction precision of the BOMP algorithm.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN929.3

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