本文選題：信道估計　切入點(diǎn)：認(rèn)知無線電　出處：《燕山大學(xué)》2014年碩士論文

【摘要】：隨著無線通信技術(shù)的發(fā)展，人們對無線頻譜資源的需求日益增長。認(rèn)知無線電技術(shù)（Cognitive Radio，CR）作為一種能夠有效地解決有限頻譜資源與日益增長的頻譜需求之間矛盾的技術(shù)手段，得到了國內(nèi)外學(xué)者的廣泛關(guān)注。其中，無線信道的信道估計方法作為認(rèn)知無線電系統(tǒng)中的關(guān)鍵技術(shù)，其性能的優(yōu)劣直接關(guān)系到認(rèn)知無線電通信質(zhì)量的好壞，因此具有重要的研究意義。 近年來，壓縮感知技術(shù)成為信號處理和無線通信領(lǐng)域里的研究熱點(diǎn)。由于無線多徑信道的時域模型可以等效為一個橫向?yàn)V波器，且其抽頭稀疏分布具有稀疏性，表明運(yùn)用壓縮感知技術(shù)進(jìn)行信道估計具備可行性。本文以降低系統(tǒng)開銷以及提高信道估計性能為目標(biāo)，研究基于壓縮感知算法的認(rèn)知無線電信道估計方法。 首先，，在對認(rèn)知無線電和壓縮感知技術(shù)的研究背景進(jìn)行介紹的基礎(chǔ)上，對現(xiàn)有傳統(tǒng)信道估計方法進(jìn)行了分析總結(jié)，并論述了運(yùn)用壓縮感知技術(shù)進(jìn)行認(rèn)知無線電信道估計的可行性。 其次，對簡化粒子群算法進(jìn)行改進(jìn)，提高了粒子群算法的全局尋優(yōu)特性，并采用改進(jìn)后的簡化粒子群算法對壓縮感知弱匹配追蹤算法進(jìn)行優(yōu)化，從而能夠快速準(zhǔn)確地搜索匹配稀疏信號的最優(yōu)原子。在信號重構(gòu)階段引入一種改進(jìn)閾值降噪策略，克服了傳統(tǒng)的硬閾值降噪和軟閾值降噪方法的不足。對某稀疏信號進(jìn)行信號處理的仿真結(jié)果驗(yàn)證了該改進(jìn)壓縮感知算法的有效性。 最后，針對非連續(xù)正交頻分復(fù)用系統(tǒng)下的無線傳輸信道估計問題，基于傳統(tǒng)的正交匹配追蹤算法，通過引入快速選擇及優(yōu)勝劣汰機(jī)制，提高了搜索最優(yōu)原子的快速性，保證了所選原子的最優(yōu)性。針對寬帶干擾和窄帶干擾兩種場景進(jìn)行的信道估計，仿真結(jié)果表明與傳統(tǒng)的最小二乘和正交匹配追蹤算法相比，本文算法所重構(gòu)出的信道與原始信道之間的均方誤差MSE更小，傳輸信號誤比特率BER更低，導(dǎo)頻數(shù)目更少，是一種更加有效地進(jìn)行無線傳輸信道參數(shù)估計的信道估計方法。
[Abstract]:With the development of wireless communication technology, the demand for wireless spectrum resources is increasing. Cognitive Radio (CR) is a kind of technology which can effectively solve the contradiction between the limited spectrum resources and the growing spectrum demand. The channel estimation method of wireless channel is a key technology in cognitive radio system, and its performance is directly related to the quality of cognitive radio communication. Therefore has the important research significance. In recent years, compression sensing technology has become a research hotspot in the field of signal processing and wireless communication, because the time-domain model of wireless multipath channel can be equivalent to a transverse filter, and its tap sparse distribution is sparse. This paper aims at reducing the system overhead and improving the performance of channel estimation, and studies the cognitive radio channel estimation method based on compressed sensing algorithm. Firstly, on the basis of introducing the research background of cognitive radio and compressed sensing technology, the existing traditional channel estimation methods are analyzed and summarized. The feasibility of cognitive radio channel estimation using compressed sensing technology is also discussed. Secondly, the simplified particle swarm optimization algorithm is improved to improve the global optimization characteristics of the particle swarm optimization algorithm, and the improved simplified particle swarm optimization algorithm is used to optimize the compression perception weak matching tracking algorithm. Thus, the optimal atom matching sparse signal can be searched quickly and accurately, and an improved threshold de-noising strategy is introduced in the signal reconstruction phase. The shortcomings of the traditional hard threshold denoising and soft threshold denoising methods are overcome. The simulation results of signal processing for a sparse signal verify the effectiveness of the improved compression sensing algorithm. Finally, aiming at the channel estimation problem of wireless transmission in discontinuous orthogonal frequency division multiplexing system, based on the traditional orthogonal matching tracking algorithm, by introducing the mechanism of quick selection and survival of the fittest, the rapidity of searching optimal atoms is improved. The channel estimation of wideband interference and narrowband interference is carried out, and the simulation results show that compared with the traditional least squares and orthogonal matching tracking algorithms, The proposed algorithm has smaller mean square error (MSE), lower bit error rate (BER) and fewer pilot frequencies between the channel and the original channel. It is a more effective channel estimation method for wireless transmission channel parameters estimation.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN925

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