本文選題：全雙工 + MU-MIMO　； 參考：《哈爾濱工業(yè)大學》2017年博士論文

【摘要】：未來的無線通信系統(tǒng)需要更可靠的、頻譜效率更有效的傳輸技術(shù),以達到更高的傳輸速率。目前提出的改善頻譜效率的方法包括多天線技術(shù)、協(xié)作網(wǎng)絡、自適應調(diào)制與編碼以及跨層設計等技術(shù)。其中,采用鏈路兩端采用天線陣列的多輸入多輸出(Multiple Input Multiple-Output,MIMO)技術(shù)能夠顯著的提高頻譜利用效率。通過增加空域維度,相互獨立的多數(shù)據(jù)流可以同時通過不同的天線傳輸,這稱之為空間復用。此外,MIMO技術(shù)還能夠提供發(fā)射分集增益和接收分解增益,利用信道的多徑特性能夠顯著的提高鏈路質(zhì)量。MIMO系統(tǒng)中不同的天線通過具有不同的多徑特性或者不同的衰落特性,使得MIMO技術(shù)在未來無線通信系統(tǒng)中具有突出的優(yōu)勢。在目前的蜂窩通信系統(tǒng)中,MIMO技術(shù)在系統(tǒng)的上下行鏈路中主要以多用戶MIMO(Multi-user MIMO,MU-MIMO)的形式存在,并且上鏈路用戶與下行鏈路用戶通過不同的頻率或者時隙與基站進行通信,即頻分雙工(Frequency Division Duplexing,FDD)和時分雙工(Time Division Duplexing,TDD)。這兩種傳輸模式稱為半雙工(Half-duplex,HD)通信。由于半雙工通信系統(tǒng)需要對時間資源或者頻率資源進行分割,其會降低頻譜利用效率。因此,與半雙工系統(tǒng)相比,全雙工(Full-duplex,FD)系統(tǒng)有潛在的優(yōu)勢,并且已在信息論、信號處理、硬件測試以及實際應用等多個方面得到了研究驗證。更進一步,全雙工通信系統(tǒng)與5G技術(shù)能夠?qū)崿F(xiàn)互補,并且能通過多種方式應用到無線通信系統(tǒng),不僅能夠提高鏈路容量、增強干擾協(xié)調(diào),還能夠支持全新的中繼協(xié)議。全雙工系統(tǒng)的主要問題是節(jié)點同時同頻收發(fā)產(chǎn)生的強自干擾。強自干擾會使得接收機的前端飽和,同時由于受到接收鏈路ADC的動態(tài)范圍的限制,期望信號的量化噪聲也會增加,所以很難對期望信號進行解碼。為了成功消除自干擾,研究人員從理論角度和實驗角度提出并設計了多種干擾消除技術(shù)。這些研究工作使得全雙工技術(shù)在短距離通信以及微蜂窩通信中得以應用。典型自干擾消除技術(shù)包括數(shù)字域干擾消除與模擬域干擾消除。值得一提的是,還有一種更復雜的干擾消除技術(shù)稱之為空域干擾消除技術(shù),并且得到了廣泛的關注�？沼蚋蓴_消除主要通過天線選擇、線性預編碼、空空間投影以及最小均方誤差濾波器(Minimum Mean Square Error)等實現(xiàn)�？沼蚋蓴_消除的本質(zhì)是利用收發(fā)機配備的多天線所提供的空間自由度完成干擾消除,在眾多空域干擾消除手段中,線性預編碼技術(shù)最具研究價值。盡管有多種自干擾消除技術(shù),但是由于硬件和算法的限制,仍然有部分與噪聲功率量級相當?shù)淖愿蓴_殘留。為了在下一代無線通信系統(tǒng)中使用全雙工技術(shù),有必要先回答如下兩個問題:首先,全雙工技術(shù)帶來的增益是什么,其次,怎樣得到這些增益。而這兩個問題的答案與實際的系統(tǒng)息息相關。為了能更好的展示全雙工技術(shù),本文將考慮單小區(qū)全雙工多用戶MIMO系統(tǒng)這種更具代表性的應用場景。因此本文的主要工作是在給定功率限制條件下,設計單小區(qū)全雙工多用戶MIMO系統(tǒng)的預編碼方案,以提高系統(tǒng)的頻譜利用效率。首先,需要面臨自干擾消除后的殘留自干擾的影響;其次,還要考慮上下行鏈路間都存在的多用戶干擾(Multi-user Interferences,MUI);最后,上行鏈路用戶的傳輸還會對下行鏈路用戶造成同信道干擾(Co-channel Interference,CCI)。為了消除掉這些干擾的影響,使用次優(yōu)、低復雜度的線性傳輸方案十分有必要,其中典型的次優(yōu)低復雜度線性傳輸方案包括:迫零(Zero Forcing,ZF)預編碼、塊對角化預編碼(Block Diagonalization,BD)、ZF接收機(ZF-R)、BD接收濾波器等。但是,這些方案會受到基站天線數(shù)以及用戶信道的限制。簡單的說,對于下行鏈路傳輸,ZF與BD預編碼的都需要基站天線數(shù)大于用戶天線數(shù)的和。只有滿足這一條件才能提供足夠的自由度,進而通過迫零等方法使得用戶處的MUI為零。此外,ZF預編碼方案在求解預編碼參數(shù)的時候還忽略了噪聲的影響。具體來說,由于全雙工通信是下一代無線通信中的一種重要傳輸模式,且下一代通信系統(tǒng)中用戶端天線數(shù)量將顯著的提升。但是ZF與BD方案在某些應用場景下無法完全利用MIMO系統(tǒng)的信道自由度。但是這些傳統(tǒng)方案的缺點可以通過了利用信泄比(Signal-to-Leakages Ratio,SLR)的方法克服。泄露的干擾信號指的是發(fā)射給目標用戶的期望信號被其他用戶接收到的部分,泄露的干擾信號的功率則用來衡量干擾的嚴重程度。這種方法的目的是使得每個用戶的期望信號的功率最大的同時,保證此用戶泄露給其他用戶的功率最小。進一步,所有用戶的預編碼變量都可以利用信泄噪比(Signal-to-Leakage-and-Noise Ratio,SLNR)為衡量標準同時進行優(yōu)化。這個衡量標準可以將耦合的優(yōu)化問題分解,并且可以得到閉合解。因此,在全雙工通信系統(tǒng)中采用SLNR的預編碼方案能夠顯著的提高系統(tǒng)的頻譜利用效率,并且不受天線數(shù)目的限制。此外,由于全雙工MU-MIMO系統(tǒng)在獨立同分布瑞利衰落信道下頻譜效率優(yōu)化問題已經(jīng)被深入的研究。但是,在實際通信系統(tǒng)中,發(fā)射即與接收機之間可能存在直射鏈路(Line-of-Sight,LOS)。尤其對于短距離或者毫米波通信,衰落信道下存在直射路徑時,通常建模為萊斯衰落模型。在數(shù)學上,經(jīng)過萊斯衰落的MIMO隨機信道矩陣是一個均值非零的復高斯矩陣,而經(jīng)過瑞利衰落的MIMO隨機信道矩陣的均值為零。所以瑞利衰落實際上可以看成萊斯衰落的特殊形式。萊斯衰落模型有直射路徑和非直射路徑(Non-Line-of-Sight,NLOS)組成。為了更符合實際系統(tǒng),評估萊斯衰落下的系統(tǒng)容量更加重要。綜上,本文將針對萊斯衰落信道進行研究。在無線通信系統(tǒng)中,信道狀態(tài)信息(Channel State Information,CSI)代表著信道鏈路的特性。這一信息描述了信號從發(fā)射機到接收機的傳播情況,其中包括散射、衰落以及信號功率隨著傳輸距離的衰減。CSI信息使得根據(jù)當前信道自適應傳輸成為可能,這對于多天線系統(tǒng)達到更高的傳輸速率十分重要。對于本文所研究的系統(tǒng)模型,我們假設基站端與用戶端都知道完整的信道狀態(tài)信息。對于基站端,其可以直接獲得上行鏈路用戶的信道狀態(tài)信息。而對于下行鏈路用戶的信道狀態(tài)信息,其有兩種獲得方式。在第一種方式中,下行鏈路用戶可以估計出CSI信息然后通過反饋鏈路將其傳輸給基站端,此種方案需要額外的反饋鏈路并且CSI的質(zhì)量與反饋信道容量有關。而對于第二種方式,當信道的相干時間遠大于信號傳輸時間時,基站可以通過信道的相互性直接對信道狀態(tài)信息進行估計,而此時則無需額外的信道開銷。對于FD-MU-MIMO單數(shù)據(jù)流傳輸系統(tǒng),只考慮自干擾與MUI而忽略CCI時,本文提出了基于SLNR的預編碼方法。對于下行鏈路,設計了應用在萊斯衰落信道下的基于SLNR的預編碼方法。對于上行鏈路,在設計基于SLNR的預編碼方法時利用了自干擾加噪聲的協(xié)方差矩陣信息。本文所研究的系統(tǒng)模型在接收端采用抑制濾波器對干擾進行消除。預編碼問題首先建模成了每個用戶的SINR最大化問題。但是由于復雜度以及多個目標函數(shù)的耦合特性,使得這一優(yōu)化問題無法求得閉合解。然而從泄露的干擾信號的角度出發(fā),可以將耦合的優(yōu)化問題解耦合,并且得到閉合解。這樣,通過對每個用戶采用廣義特征值分解(General Eigenvalue Decomposition,GECD)即可得到使得SLNR最大化的最優(yōu)預編碼器。進而可以分別得到上下行鏈路的和速率,然后,將上下行鏈路的和速率相加即可得到FD-MU-MIMO系統(tǒng)的頻譜利用效率。對于只考慮自干擾與MUI時的FD-MU-MIMO多數(shù)據(jù)流傳輸系統(tǒng),同樣也利用基于信泄噪比的預編碼思想。與單數(shù)據(jù)流傳輸時一樣,當利用多數(shù)據(jù)流傳輸時,對于下行鏈路,可以直接設計基于SLNR的預編碼方法,對于上行鏈路,在設計的基于SLNR的預編碼時也需要利用了自干擾加噪聲的協(xié)方差矩陣信息。但是在設計預編碼之前,需要利用信道狀態(tài)信息以及接收端的匹配濾波器對耦合的多數(shù)據(jù)流進行解耦合。這樣通過引入額外的設計約束條件,在功率受限時的優(yōu)化問題可以建模為所有用戶的SLNR同時最大化問題。目標函數(shù)同樣可以通過GEVD進行處理。帶入得到預編碼參數(shù),既可以得到多數(shù)據(jù)流傳輸時的FD-MU-MIMO系統(tǒng)的頻譜利用效率。對于本文所研究的FD-MU-MIMO系統(tǒng),當自干擾、MUI以及CCI同時存在,且每個用戶處于單數(shù)據(jù)流傳輸狀態(tài)時,本文提出了一種改進的基于SLNR的預編碼方案。對于下行鏈路,且給定接收機結(jié)構(gòu)時,本文設計的預編碼方案通過在基站端利用CCI的協(xié)方差矩陣信息,能夠在抑制同信道干擾的同時使得下行鏈路的和速率最大化。接收端則采用了主分量分析(Principal Component Analysis,PCA)白化濾波器來抑制干擾。白化矩陣可以通過對同信道干擾加噪聲的協(xié)方差矩陣進行特征矢量分解后得到。此外,對于上行鏈路,設計了利用自干擾信息的基于SLNR的預編碼方案。接收端同樣利用了主分量分析抑制濾波器來消除干擾,并且在發(fā)射端設計預編碼時只采用自干擾加噪聲的協(xié)方差矩陣。優(yōu)化問題建模成使得所有用戶的SLNR同時最大即可得到最優(yōu)的預編碼參數(shù)。利用GEVD處理即可對優(yōu)化問題進行求解。進而可以得到整個系統(tǒng)的頻譜利用效率。當用戶配有多個天線,且利用多數(shù)據(jù)流傳輸,同時還存在自干擾、MUI以及CCI時,本文提出了一種利用CCI的改進的基于SLNR的下行鏈路預編碼方法。系統(tǒng)在接收端采用白化濾波器對干擾進行抑制,并且在預編碼時使用了CCI加噪聲協(xié)方差矩陣。對于上行鏈路,同樣采用了利用自干擾加噪聲協(xié)方差矩陣信息的預編碼方法。為了將多數(shù)據(jù)流解耦合,上下行鏈路的接收端都采用了一種簡單的解碼方案。而目標函數(shù)同樣利用GEVD進行處理。通過帶入得到的預編碼參數(shù),可以得到分別得到上下行鏈路的和速率,進而得到系統(tǒng)的頻譜利用效率。本文所研究的全雙工系統(tǒng)的頻譜利用效率都將與半雙工系統(tǒng)進行對比。半雙工MU-MIMO系統(tǒng)的頻譜利用效率是在沒有自干擾時的上行和速率和下行和速率的總和的一半。仿真結(jié)果顯示,當基站天線數(shù)固定且基站發(fā)射功率和用戶功率都不是很大時,與半雙工MU-MIMO相比,全雙工MU-MIMO能夠顯著的提升系統(tǒng)的頻譜利用效率。這是因為當發(fā)射功率較小時,系統(tǒng)內(nèi)的CCI與自干擾不是很大。此外,對于每個用戶處于多數(shù)據(jù)流傳輸?shù)那闆r,當固定基站的天線個數(shù),增加下行鏈路每個用戶的天線個數(shù)時,采用SLNR預編碼時的頻譜效率也會隨之增加。但是,對于BD預編碼與ZF預編碼,當基站采用相同的天線個數(shù),隨著用戶天線數(shù)的增加,頻譜利用效率將會下降。這是因為這兩種方案存在維度限制,無法利用所有的信道自由度,每個用戶只能采用單數(shù)據(jù)流傳輸。所以,很顯然會帶來容量損失。當自干擾以及同信道干擾的功率很小時,全雙工MU-MIMO系統(tǒng)在萊斯衰落信道下的性格更好。
[Abstract]:The future wireless communication system needs more reliable, more efficient transmission technology to achieve higher transmission rate. The present methods to improve the spectrum efficiency include multi antenna technology, cooperative network, adaptive modulation and coding and cross layer design. Multiple Input Multiple-Output (MIMO) technology can significantly improve the efficiency of spectrum utilization. By adding space dimensions, independent multiple data streams can be transmitted simultaneously through different antennas. This is called spatial multiplexing. In addition, MIMO technology can also provide transmit diversity gain and receive decomposition gain, using multiple channels. The path characteristics can significantly improve the link quality.MIMO system with different multipath characteristics or different fading characteristics, which makes MIMO technology have a prominent advantage in the future wireless communication system. In the current cellular communication system, the MIMO technology is mainly multiuser MIMO (Mu) in the system's upper and lower links. The form of lti-user MIMO, MU-MIMO), and the link users and downlink users communicate with the base station through different frequencies or slots, that is, frequency division duplex (Frequency Division Duplexing, FDD) and time division duplex (Time Division Duplexing, TDD). These two transmission modes are called half duplex (Half-duplex,) communication. Duplex communication systems need to divide time resources or frequency resources, which can reduce the efficiency of spectrum utilization. Therefore, compared with the semi duplex system, full duplex (Full-duplex, FD) system has potential advantages, and has been studied and verified in many aspects such as information theory, signal processing, hardware testing and practical applications. The full duplex communication system and 5G technology can be complementation, and can be applied to wireless communication systems in many ways. It not only improves the link capacity, enhances interference coordination, but also supports a new relay protocol. The main problem of the full duplex system is the strong self interference produced by the same frequency generation at the same time. Strong self interference will enable the receiver to receive the receiver. The front end of the machine is saturated. At the same time, due to the limitation of the dynamic range of the receiving link ADC, the desired signal quantization noise will also increase, so it is difficult to decode the desired signal. In order to successfully eliminate self interference, the researchers have proposed and designed various interference elimination techniques from the theoretical and experimental angles. These research work makes all of the research work. Duplex technology is used in short distance communication and microcellular communication. Typical self jamming elimination techniques include digital domain interference cancellation and analog domain interference cancellation. It is worth mentioning that there is a more complex interference cancellation technique called space interference cancellation, and a wide range of attention has been paid. Over antenna selection, Linear Precoding, space space projection and Minimum Mean Square Error are implemented. The essence of space interference cancellation is to eliminate interference by the spatial freedom provided by the multiple antennas equipped by the transceiver. The Linear Precoding technology has the most research price in many airspace interference elimination methods. Although there are a variety of self interference elimination techniques, there are still some self interference residues that are equal to the magnitude of the noise power due to the constraints of hardware and algorithms. In order to use full duplex technology in the next generation wireless communication system, it is necessary to answer two questions: first, what is the gain of full duplex technology, and then how The answers to these two problems are closely related to the actual system. In order to better display full duplex technology, this paper will consider the more representative application scenario of the single cell full duplex multiuser MIMO system. Therefore, the main work of this paper is to design a single cell full duplex and multiple use under a given power limit. The precoding scheme of the MIMO system is designed to improve the efficiency of the spectrum utilization. First, it needs to face the influence of self interference after the self interference cancellation; secondly, the multiuser interference (Multi-user Interferences, MUI) exists between the uplink and the downlink; finally, the transmission of the uplink users will also cause the same to the downlink users. Channel interference (Co-channel Interference, CCI). In order to eliminate the impact of these disturbances, it is necessary to use suboptimal, low complexity linear transmission schemes, in which the typical suboptimal and low complexity linear transmission schemes include zero forcing (Zero Forcing, ZF) precoding, block diagonalization precoding (Block Diagonalization, BD), ZF receiver (ZF-R), B. D receive filters, etc., however, these schemes will be limited by the number of base station antennas and the user channel. Simply, for downlink transmission, both ZF and BD precoding need the sum of the base station antenna number greater than the number of the user antenna. Only to satisfy this condition can the sufficient self degree be provided, and then the user can be made by the method of forcing zero and so on. MUI is zero. In addition, the ZF precoding scheme ignores the effect of noise when solving the precoding parameters. In particular, due to the full duplex communication is an important transmission mode in the next generation wireless communication, and the number of user terminal antennas in the next generation communication system will be significantly improved. But the ZF and BD schemes are in some application scenarios. The channel freedom of the MIMO system can not be fully utilized, but the shortcomings of these traditional schemes can be overcome by using the method of Signal-to-Leakages Ratio (SLR). The leaked interference signal refers to the part of the desired signal transmitted to the target user by other users, and the power of the leaked interference signal is used to balance the power of the interference signal. The purpose of this method is to make the maximum power of the desired signal for each user and to ensure that the power of the user is minimal to other users. Further, all users' precoding variables can be optimized using the Signal-to-Leakage-and-Noise Ratio (SLNR) as a measurement standard. This measure can decompose the coupling optimization problem and get the closed solution. Therefore, the SLNR precoding scheme in the full duplex communication system can significantly improve the spectral efficiency of the system and not be restricted by the number of antennas. In addition, the full duplex MU-MIMO system is independent and distributed Rayleigh fading letters. The problem of spectral efficiency optimization has been studied in depth. However, in the actual communication system, there may be a direct link (Line-of-Sight, LOS) between the transmitter and the receiver. Especially for short distance or millimeter wave communication, when there is a direct path in the fading channel, through Chang Jianmo as a leas fading model. The fading MIMO random channel matrix is a mean nonzero complex Gauss matrix, and the mean value of the MIMO random channel matrix through the Rayleigh fading is zero. So the Rayleigh fading can actually look at the special form of the Rayleigh fading. The Leos fading model consists of the direct path and the non direct path (Non-Line-of-Sight, NLOS). In the actual system, it is more important to evaluate the system capacity under the Leth fading. In this paper, we will study the leas fading channel. In the wireless communication system, the channel state information (Channel State Information, CSI) represents the characteristics of the channel link. This information describes the transmission of the signal from the transmitter to the receiver, including the transmission of the signal from the transmitter to the receiver. Scattering, fading and the attenuation of signal power with the transmission distance.CSI information makes it possible to adapt to the current channel adaptive transmission, which is very important for the higher transmission rate of the multi antenna system. For the system model studied in this paper, we assume that the base station end and the user end are aware of the complete channel state information. The base station end can directly obtain the channel state information of the uplink users, and there are two ways to obtain the channel status information of the downlink users. In the first way, the downlink user can estimate the CSI information and transmit it to the base station via a feedback link, which requires an additional feedback link. And the quality of CSI is related to the capacity of the feedback channel. For the second way, when the coherent time of the channel is far greater than the signal transmission time, the base station can estimate the channel state information directly through the channel interaction, but at this time there is no additional channel overhead. For the FD-MU-MIMO single data stream transmission system, it only considers self drying. When disturbing MUI and ignoring CCI, this paper proposes a precoding method based on SLNR. For downlink, a precoding method based on SLNR is designed for the downlink. For uplink, the covariance matrix information of self jamming plus noise is used in the design of a SLNR based precoding method. The system model studied in this paper is used in this paper. The precoding problem is first modeled as the SINR maximization problem for each user. However, due to the complexity and the coupling characteristics of multiple target functions, the optimization problem can not be closed. However, the coupling can be obtained from the angle of the leakage signal. The optimization problem is decoupled and a closed solution is obtained. By using the generalized eigenvalue decomposition (General Eigenvalue Decomposition, GECD) for each user, the optimal precoder that maximizes the SLNR can be obtained. Then the up and down link and the rate can be obtained respectively. Then, the up and down link and the rate can be added to get the F. The spectrum utilization efficiency of the D-MU-MIMO system. For the FD-MU-MIMO multi data stream transmission system with only self interference and MUI, the idea of precoding based on the signal to noise ratio is also used. As with the single data stream transmission, when the multi data stream is transmitted, the SLNR based precoding method can be directly designed for the downlink. It is necessary to use the covariance matrix information of self interference and noise in the design of SLNR based precoding. But before the design of precoding, it is necessary to use the channel state information and the receiver's matched filter to decouple the coupled multi data stream. The limited optimization problem can be modeled as the SLNR simultaneous maximization problem for all users. The target function can also be processed by GEVD. To get the precoding parameters, we can get the spectrum utilization efficiency of the FD-MU-MIMO system when the multi data stream is transmitted. For the FD-MU-MIMO system studied in this paper, when self interference, MUI and CCI At the same time, and when each user is in a single data stream transmission state, an improved SLNR based precoding scheme is proposed. For downlink, and given the receiver structure, the precoding scheme designed in this paper can make use of the covariance matrix of CCI at the base station end to suppress the same channel interference at the same time. The link and rate are maximized. The receiver uses the Principal Component Analysis (PCA) whitening filter to suppress interference. The whitening matrix can be obtained by decomposing the eigenvector of the covariance matrix with the noise and interference of the same channel. Furthermore, the base of the self interference information is designed for the uplink. In the precoding scheme of SLNR, the receiver also uses the principal component analysis suppression filter to eliminate the interference, and only uses the covariance matrix of self interference and noise when the transmitter is precoded. The optimization problem is modeled to make the SLNR at the same time the best precoding parameters of all users. The GEVD processing can be used to solve the problem. The optimization problem is solved. Then the spectrum utilization efficiency of the whole system can be obtained. When the user is equipped with multiple antennas, and using multiple data streams, and there are self interference, MUI and CCI, a improved SLNR based downlink precoding method using CCI is proposed. Interference is suppressed, and CCI plus noise covariance matrix is used in precoding. For uplink, self interference and noise are also adopted.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TN919.3
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本文編號：1895027