發(fā)布時間：2019-01-02 07:21

【摘要】：多天線系統(tǒng)(MIMO)利用傳輸分集、空間復(fù)用等技術(shù)充分挖掘維度資源,提高傳輸效率和通信質(zhì)量。隨著通信技術(shù)的發(fā)展,4G蜂窩網(wǎng)絡(luò)中的多用戶MIMO并不能對頻譜效率和能量效率有數(shù)量級的提升,并且為滿足通信的大容量、低功耗、低成本要求,未來5G網(wǎng)絡(luò)提出在基站端布置大量的天線,在相同的時頻資源塊上服務(wù)多個小區(qū)用戶,增加有用信號的功率,從而增加信干比,能夠顯著克服信道衰落和噪聲的影響,使得基站處理能力得到顯著提升。論文首先介紹了 TDD和FDD兩種Massive MIMO幀結(jié)構(gòu),并闡述了 Massive MIMO使用TDD模式原因,然后對Massive MIMO TDD系統(tǒng)模型和導(dǎo)頻污染進行介紹,詳細(xì)分析了系統(tǒng)上行導(dǎo)頻傳輸和信道估計,上行數(shù)據(jù)傳輸和MRC檢測,下行數(shù)據(jù)接收過程,并搭建Massive MIMO系統(tǒng)仿真平臺,給出仿真流程圖分析了傳統(tǒng)導(dǎo)頻設(shè)計的LS估計和MMSE估計性能。然后在Massive MIMO系統(tǒng)框架下介紹了單小區(qū)半正交導(dǎo)頻設(shè)計原理,提出半正交導(dǎo)頻修正方案。給出修正后導(dǎo)頻的設(shè)計原理和幀結(jié)構(gòu),并對導(dǎo)頻設(shè)計的上下行傳輸過程以及性能進行研究,將其與傳統(tǒng)導(dǎo)頻和半正交導(dǎo)頻進行性能對比,給出了導(dǎo)頻設(shè)計方案仿真流程圖,在之前建立的Massive MIMO仿真平臺對不同導(dǎo)頻設(shè)計方案進行性能對比。最后介紹兩種多小區(qū)協(xié)作信道估計方式。第一種為基于Bayes估計的協(xié)作式信道估計。首先介紹Bayes估計原理,分析Bayes估計均方誤差,到達(dá)角和協(xié)方差矩陣的影響,然后根據(jù)Bayes估計提出一種協(xié)作式信道估計策略,將用戶分組,找到信道估計均方誤差最小的一組用戶同時進行信道估計;第二種為基于TCGTR的協(xié)作式信道估計,此方法為第二章單小區(qū)半正交導(dǎo)頻設(shè)計的擴展,將其運用于多小區(qū)系統(tǒng),對TCGTR估計過程進行了詳細(xì)闡述;最后將兩種估計方法進行仿真驗證,其性皆優(yōu)于傳統(tǒng)信道估計方式。
[Abstract]:Multi-antenna system (MIMO) exploits dimensionality resources by means of transmission diversity and spatial multiplexing to improve transmission efficiency and communication quality. With the development of communication technology, multiuser MIMO in 4G cellular network can not improve spectrum efficiency and energy efficiency by an order of magnitude, and to meet the requirements of large capacity, low power consumption and low cost, In the future 5G network proposes to deploy a large number of antennas at the base station to serve multiple cell users on the same time-frequency resource block to increase the power of useful signals, thus increasing the signal-to-interference ratio, which can significantly overcome the influence of channel fading and noise. So that the base station processing capacity has been significantly improved. In this paper, two kinds of Massive MIMO frame structures, TDD and FDD, are introduced, and the reason why Massive MIMO uses TDD mode is expounded. Then, the Massive MIMO TDD system model and pilot pollution are introduced, and the uplink pilot transmission and channel estimation are analyzed in detail. Uplink data transmission, MRC detection, downlink data receiving process, and Massive MIMO system simulation platform are built. The simulation flowchart is given to analyze the LS estimation and MMSE estimation performance of the traditional pilot design. Then, the design principle of semi-orthogonal pilot in single cell is introduced under the framework of Massive MIMO system, and a semi-orthogonal pilot correction scheme is proposed. The design principle and frame structure of modified pilot are given, and the transmission process and performance of pilot design are studied. The performance of pilot design is compared with that of traditional pilot and semi-orthogonal pilot, and the simulation flow chart of pilot design scheme is given. The performance of different pilot design schemes is compared with the previous Massive MIMO simulation platform. Finally, two methods of multi-cell cooperative channel estimation are introduced. The first is cooperative channel estimation based on Bayes estimation. Firstly, the principle of Bayes estimation is introduced, and the effects of mean square error, angle of arrival and covariance matrix of Bayes estimation are analyzed. Then, a cooperative channel estimation strategy based on Bayes estimation is proposed to group users. A group of users with the least mean square error of channel estimation is found to estimate the channel at the same time. The second is cooperative channel estimation based on TCGTR. This method is an extension of single cell semi-orthogonal pilot design in chapter 2. It is applied to multi-cell system and the process of TCGTR estimation is described in detail. Finally, the two estimation methods are verified by simulation, and their performance is better than the traditional channel estimation method.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN919.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張陽;;面向5G的大規(guī)模天線無線傳輸理論與技術(shù)[J];通訊世界;2016年22期

2 Dongming WANG;Yu ZHANG;Hao WEI;Xiaohu YOU;Xiqi GAO;Jiangzhou WANG;;An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications[J];Science China(Information Sciences);2016年08期

3 Cheng-Xiang WANG;Shangbin WU;Lu BAI;Xiaohu YOU;Jing WANG;Chih-Lin I;;Recent advances and future challenges for massive MIMO channel measurements and models[J];Science China(Information Sciences);2016年02期

4 Guo Mangqing;Xie Gang;Gao Jinchun;Liu Yuan'an;;Enhanced EVD based channel estimation and pilot decontamination for Massive MIMO networks[J];The Journal of China Universities of Posts and Telecommunications;2015年06期

5 徐鳳陽;王東;肖揚;寇金鋒;;大規(guī)模MIMO系統(tǒng)中基于子空間跟蹤的半盲信道估計[J];應(yīng)用科學(xué)學(xué)報;2015年05期

6 胡瑩;黃永明;俞菲;楊綠溪;;多用戶大規(guī)模MIMO系統(tǒng)能效資源分配算法[J];電子與信息學(xué)報;2015年09期

7 戚晨皓;黃永明;金石;;大規(guī)模MIMO系統(tǒng)研究進展[J];數(shù)據(jù)采集與處理;2015年03期

8 李新民;邱玲;;大規(guī)模MIMO系統(tǒng)中基于溢出概率的魯棒協(xié)作波束設(shè)計[J];電子與信息學(xué)報;2015年04期

9 顧浙騏;張忠培;;大規(guī)模MIMO時分雙工系統(tǒng)的基站天線互易校準(zhǔn)算法[J];電子與信息學(xué)報;2015年02期

10 王海榮;王玉輝;黃永明;楊綠溪;;大規(guī)模MIMO蜂窩網(wǎng)絡(luò)系統(tǒng)中的導(dǎo)頻污染減輕方法[J];通信學(xué)報;2014年01期

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