【摘要】：5G是面向2020年以后移動通信需求而發(fā)展的新一代移動通信系統(tǒng),其核心技術(shù)包括高能效的無線傳輸技術(shù)以及高密度無線網(wǎng)絡(luò)技術(shù),其中重點(diǎn)表現(xiàn)為大規(guī)模MIMO技術(shù)。大規(guī)模MIMO系統(tǒng)具有超高的頻譜利用率和能量效率等優(yōu)點(diǎn),自被提出之后就獲得了廣泛而深入的研究,是當(dāng)前通信領(lǐng)域研究的重點(diǎn)內(nèi)容。然而,大規(guī)模MIMO系統(tǒng)實(shí)現(xiàn)超高性能增益的前提是系統(tǒng)可以正確地獲得信道狀態(tài)信息,隨著發(fā)射天線數(shù)增多,大規(guī)模MIMO系統(tǒng)無法承擔(dān)信道空間相關(guān)信息的反饋開銷,此時訓(xùn)練序列的計(jì)算發(fā)生在用戶設(shè)備端而非基站端,基站由于不具備信道長期統(tǒng)計(jì)信息而需要不斷接收反饋回來的訓(xùn)練信號。本文正是針對大規(guī)模MIMO系統(tǒng)中基站不具備信道長期統(tǒng)計(jì)信息這一問題,研究FDD大規(guī)模MIMO系統(tǒng)中下行鏈路的訓(xùn)練算法優(yōu)化問題。對于傳統(tǒng)訓(xùn)練算法在大規(guī)模MIMO系統(tǒng)存在著性能上的限制。本文研究了開環(huán)訓(xùn)練算法對于大規(guī)模MIMO系統(tǒng)的適用性問題,在推導(dǎo)了開環(huán)單射情況下使得信道估計(jì)均方誤差最小的訓(xùn)練序列結(jié)構(gòu)后,針對優(yōu)化后的訓(xùn)練序列,本文通過理論推導(dǎo)和實(shí)際仿真證明了開環(huán)單射訓(xùn)練方法存在的天花板效應(yīng),大規(guī)模MIMO系統(tǒng)中訓(xùn)練序列固定時系統(tǒng)的歸一化接收信噪比將受到限制。使用開環(huán)有記憶的訓(xùn)練方法可以緩解開環(huán)單射訓(xùn)練所存在的性能限制,使用卡爾曼濾波來預(yù)測信道的變化信息可以提升大規(guī)模MIMO系統(tǒng)中信道估計(jì)的準(zhǔn)確度。為了解決大規(guī)模MIMO系統(tǒng)中基站端無法完成訓(xùn)練序列計(jì)算的問題,本文重點(diǎn)研究了大規(guī)模MIMO系統(tǒng)中的閉環(huán)有記憶訓(xùn)練算法。研究了使得信道估計(jì)性能最好的訓(xùn)練序列結(jié)構(gòu)設(shè)計(jì)方法,通過尋找高性能的訓(xùn)練信號集以及使得MSE最小的全反饋訓(xùn)練信號,使得信道估計(jì)的性能得到提升。仿真結(jié)果表明閉環(huán)有記憶訓(xùn)練方法可以在只增加幾比特反饋開銷的前提下使得信道估計(jì)更準(zhǔn)確,當(dāng)用戶端反饋全部的訓(xùn)練信號結(jié)構(gòu)時,系統(tǒng)的下行鏈路可以進(jìn)一步獲得更高的信道估計(jì)性能。本文創(chuàng)新性地提出了引入功率分配策略的閉環(huán)有記憶訓(xùn)練算法,基站可以在不具備信道長期統(tǒng)計(jì)信息的前提下完成更加準(zhǔn)確的下行鏈路信道估計(jì)。通過計(jì)算機(jī)仿真評估了新算法的信道估計(jì)均方誤差性能指標(biāo)。提出的算法可以在系統(tǒng)性能與鏈路開銷取得較好的折中,適用于大規(guī)模MIMO系統(tǒng)中的訓(xùn)練設(shè)計(jì)問題。
[Abstract]:5G is a new generation of mobile communication system, which is developed to meet the requirement of mobile communication after 2020. Its core technologies include high energy efficiency wireless transmission technology and high density wireless network technology, in which the emphasis is on large-scale MIMO technology. Large-scale MIMO system has the advantages of ultra-high spectral efficiency and energy efficiency, and has been widely and deeply studied since it was proposed, which is the focus of the research in the field of communication. However, the premise of realizing super-high performance gain in large-scale MIMO systems is that the channel state information can be obtained correctly by the system. With the increase of the number of transmitting antennas, the large-scale MIMO system cannot afford the feedback overhead of the space-related information of the channel. In this case, the calculation of the training sequence takes place at the end of the user equipment rather than the base station, and the base station needs to receive the feedback training signal because of the lack of long-term statistical information of the channel. Aiming at the problem that the base station does not have the long-term statistical information of the channel in the large-scale MIMO system, this paper studies the optimization of the downlink training algorithm in the FDD large-scale MIMO system. There is a limitation on the performance of traditional training algorithms in large scale MIMO systems. In this paper, the applicability of open-loop training algorithm to large-scale MIMO systems is studied. After the structure of training sequence with minimum mean square error of channel estimation is derived in the case of open-loop monojection, the optimized training sequence is proposed. In this paper, the ceiling effect of open-loop single-shot training method is proved by theoretical derivation and practical simulation, and the normalized reception SNR of large-scale MIMO system is limited when the training sequence is fixed. The performance limitation of open-loop single-shot training can be alleviated by using open-loop memory training method. Using Kalman filter to predict channel variation information can improve the accuracy of channel estimation in large-scale MIMO systems. In order to solve the problem that the base station can not complete the training sequence calculation in the large-scale MIMO system, this paper focuses on the closed-loop memory training algorithm in the large-scale MIMO system. The training sequence structure design method which makes the channel estimation performance the best is studied. The performance of channel estimation is improved by searching for the high performance training signal set and making the MSE minimum full feedback training signal. The simulation results show that the closed-loop memory training method can make the channel estimation more accurate under the condition of adding only a few bits of feedback overhead. The downlink of the system can further achieve higher channel estimation performance. In this paper a novel closed-loop memory training algorithm with power allocation strategy is proposed. The base station can complete more accurate downlink channel estimation without long-term statistical information of the channel. The performance index of channel estimation mean square error of the new algorithm is evaluated by computer simulation. The proposed algorithm can achieve a good compromise between system performance and link overhead, and is suitable for training design in large-scale MIMO systems.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN919.3

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