本文選題：MIMO-OFDM + 信道估計(jì)　； 參考：《蘭州交通大學(xué)》2016年碩士論文

【摘要】：自人類誕生開(kāi)始,我們從未放棄對(duì)通信技術(shù)的研究。在2004年12月的3GPP多倫多會(huì)議上,LTE(Long Term Evolution,長(zhǎng)期演進(jìn))正式立項(xiàng)并啟動(dòng),標(biāo)志著第四代移動(dòng)通信(4G)技術(shù)的時(shí)代已經(jīng)到來(lái)。LTE是3G的演進(jìn)技術(shù),采用OFDM(正交頻分復(fù)用)技術(shù)和MIMO(多輸入多輸出)技術(shù)作為唯一標(biāo)準(zhǔn),堪稱目前民用移動(dòng)通信領(lǐng)域的頂尖技術(shù)。MIMO-OFDM無(wú)線通信系統(tǒng)憑借高信道容量和高頻譜利用率已經(jīng)被廣泛地應(yīng)用于需要高速、大數(shù)據(jù)量的通信場(chǎng)合。MIMO-OFDM系統(tǒng)優(yōu)勢(shì)顯著,但缺點(diǎn)也不容忽視,那就是在信息的獲取環(huán)節(jié)極其依賴信道的狀態(tài)信息參數(shù)。所以信道估計(jì)作為MIMO-OFDM無(wú)線通信系統(tǒng)中的關(guān)鍵步驟,算法的好壞會(huì)直接影響到整個(gè)系統(tǒng)的性能表現(xiàn)。所謂信道估計(jì),就是利用導(dǎo)頻對(duì)無(wú)線信道進(jìn)行采樣,并在系統(tǒng)接收端應(yīng)用相應(yīng)的恢復(fù)算法計(jì)算出信道參數(shù)。信道估計(jì)效果的好壞,決定著信息傳遞質(zhì)量的高低。無(wú)線信道環(huán)境非常復(fù)雜,參數(shù)并不像有線信道那樣固定可預(yù)見(jiàn)。在某些環(huán)境下,缺少信道估計(jì)環(huán)節(jié)的無(wú)線通信系統(tǒng)的性能會(huì)受到很大影響。在傳統(tǒng)的MIMO-OFDM系統(tǒng)信道估計(jì)算法中,導(dǎo)頻的放置均必須滿足奈奎斯特定理。對(duì)于快速變化的無(wú)線信道,這意味著系統(tǒng)需要傳輸大量的導(dǎo)頻信號(hào),占用大量的頻帶資源。盡管OFDM技術(shù)可以提高系統(tǒng)的頻譜效率,但如果節(jié)省下的頻帶資源被不攜帶任何信息的導(dǎo)頻信號(hào)占用,節(jié)省出來(lái)的頻段將被白白浪費(fèi),這與MIMO-OFDM系統(tǒng)設(shè)計(jì)的初衷背道而馳。壓縮感知理論指出,若被采樣信號(hào)是稀疏的,那么就能夠以遠(yuǎn)低于奈奎斯特定理所需的采樣值個(gè)數(shù)采樣信號(hào),并通過(guò)重構(gòu)算法精確地恢復(fù)出原始信號(hào)。本文研究的最終目的是減少信道估計(jì)所需傳輸?shù)膶?dǎo)頻數(shù)量,節(jié)省系統(tǒng)的頻帶資源,同時(shí)不影響系統(tǒng)的整體性能。由于大多數(shù)MIMO-OFDM信道具有稀疏或近似稀疏的信道參數(shù),因此本文應(yīng)用壓縮感知理論代替奈奎斯特定理對(duì)信道參數(shù)進(jìn)行采樣,并運(yùn)用壓縮感知重構(gòu)算法重構(gòu)信道參數(shù)。為了在確保估計(jì)性能的同時(shí)彌補(bǔ)隨機(jī)導(dǎo)頻的不足,本文設(shè)計(jì)了一種確定導(dǎo)頻來(lái)代替?zhèn)鹘y(tǒng)的均勻分布和隨機(jī)導(dǎo)頻。仿真結(jié)果表明,確定導(dǎo)頻與隨機(jī)導(dǎo)頻保持了相同的信道估計(jì)性能。雖然在傳統(tǒng)的MIMO-OFDM信道估計(jì)中,均勻分布的導(dǎo)頻可以為系統(tǒng)帶來(lái)最佳的性能表現(xiàn),但在基于壓縮感知的算法中,均勻分布的導(dǎo)頻卻不能得到很好的信道估計(jì)效果。最后的仿真實(shí)驗(yàn)結(jié)果顯示,基于壓縮感知的MIMO-OFDM信道估計(jì)算法不僅可以使得所需傳送的導(dǎo)頻數(shù)量大大減少,同時(shí)又可以確保高精確度的估計(jì)性能,達(dá)到本論文題目的研究目的。
[Abstract]:Since the birth of mankind, we have never given up our research on communication technology. At the 3GPP Toronto Conference in December 2004, LTEL long term Evolution (long term Evolution) was formally proposed and launched, indicating that the era of the fourth generation mobile communication 4G) technology has arrived. LTE is the evolution technology of 3G. OFDM (orthogonal Frequency Division Multiplexing) and Mimo (multiple input and multiple output) technologies are used as the sole standard. MIMO-OFDM wireless communication system, which can be regarded as the top technology in the field of civil mobile communication at present, has been widely used in communication field with high channel capacity and high spectral efficiency, which requires high speed and large amount of data. MIMO-OFDM system has a significant advantage. However, the disadvantage can not be ignored, that is, the acquisition of information is extremely dependent on the channel state information parameters. Therefore, as a key step in MIMO-OFDM wireless communication system, channel estimation will directly affect the performance of the whole system. The so-called channel estimation is to sample the wireless channel by using pilot frequency and calculate the channel parameters by using the corresponding recovery algorithm at the receiver of the system. The quality of channel estimation determines the quality of information transmission. The wireless channel environment is very complex, the parameters are not as fixed and predictable as the wired channel. In some environments, the performance of wireless communication systems without channel estimation will be greatly affected. In the traditional channel estimation algorithms for MIMO-OFDM systems, pilot placement must satisfy Nyquist's theorem. For rapidly changing wireless channels, this means that the system needs to transmit a large number of pilot signals and occupy a large amount of frequency band resources. Although OFDM technology can improve the spectral efficiency of the system, if the saved frequency band resources are occupied by pilot signal without any information, the saved frequency band will be wasted, which is contrary to the original intention of MIMO-OFDM system design. Compression sensing theory points out that if the sampled signal is sparse, the sample signal can be sampled at a number far below the sampling value required by Nyquist's theorem, and the original signal can be accurately recovered by the reconstruction algorithm. The ultimate purpose of this paper is to reduce the number of pilots needed for channel estimation and to save the frequency band resources of the system without affecting the overall performance of the system. Because most MIMO-OFDM channels have sparse or nearly sparse channel parameters, compression sensing theory is used instead of Nyquist theorem to sample channel parameters, and compression perception reconstruction algorithm is used to reconstruct channel parameters. In order to ensure the performance of estimation and make up for the deficiency of random pilot, a new method of determining pilot is designed to replace the traditional uniform distribution and random pilot. The simulation results show that the performance of channel estimation is the same as that of random pilot. Although in the traditional MIMO-OFDM channel estimation, the uniformly distributed pilot can bring the best performance for the system, but in the compressed sensing algorithm, the uniformly distributed pilot can not get a good channel estimation effect. Finally, the simulation results show that the compressed sensing based MIMO-OFDM channel estimation algorithm can not only greatly reduce the number of pilots to be transmitted, but also ensure the high accuracy of the estimation performance.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN919.3

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