空間干擾對齊的信號檢測與性能分析研究
發(fā)布時間：2018-07-17 07:03
【摘要】：無線通信的主要目標(biāo)之一是提高系統(tǒng)的頻譜效率,而干擾則是實現(xiàn)該目標(biāo)的主要限制因素之一,它不但影響頻譜效率的提高,而且缺乏有效的處理方案。最近,關(guān)于干擾處理技術(shù)的研究取得了較大進(jìn)展,即干擾對齊(Interference Alignment,IA)。研究表明干擾不是無線通信的根本限制。理論上,IA在干擾信道中,可以獲得K/2倍的復(fù)用增益,其中K為干擾信道中的用戶數(shù),即IA的信道容量可以與用戶數(shù)成線性關(guān)系,這使得IA引起了廣泛的關(guān)注。IA通過預(yù)編碼技術(shù),使得在每個目的節(jié)點,所有的干擾相互重疊,從而為期望信號提供無干擾的信號維度,使徹底消除干擾對期望信號的影響成為可能。本學(xué)位論文的主要研究內(nèi)容包括四個方面：IA的閉式預(yù)編碼算法、迭代預(yù)編碼算法、分集算法以及基于隨機矩陣?yán)碚摰腎A性能分析。內(nèi)容安排如下：首先,給出了實現(xiàn)干擾對齊的條件,并證明了干擾對齊解的非唯一性。在某些情況下,干擾對齊的預(yù)編碼矩陣可以直接獲得,但由于干擾對齊的目標(biāo)函數(shù)并不是信道容量,且IA問題的多解性,使得IA解的優(yōu)化比較困難。針對這個問題,分析了IA中期望信號與干擾子空間的內(nèi)積范數(shù)的特點,得到了二者的內(nèi)積范數(shù)與處理后期望信號功率的關(guān)系。提出了內(nèi)積范數(shù)最小的干擾對齊算法,分析表明該算法可以優(yōu)化期望信號能量,從而提高系統(tǒng)的信道容量。注意到IA的閉式預(yù)編碼矩陣只能在特定場景下才能獲得,一般情況下,IA的預(yù)編碼矩陣只能通過迭代方法獲得。IA的迭代算法首先建立一個目標(biāo)函數(shù),通過迭代最小化該目標(biāo)函數(shù)來得到預(yù)編碼矩陣。典型的迭代干擾對齊算法是分布式IA,而最大信干噪比(Max-Signal-to-Interference-Noise-Ratio, Max-SINR)算法可以在一定程度上作為干擾信道的容量限。Max-SINR算法的主要問題是不能保證收斂,而且復(fù)雜度也很高。通過研究Max-SINR算法不收斂的原因,同時又借鑒了該算法的思想,分析干擾子空間及期望信號子空間的各自特點及相互關(guān)系,提出以干擾與期望信號泄漏出相應(yīng)子空間功率的加權(quán)和作為目標(biāo)函數(shù),通過迭代最小化該目標(biāo)函數(shù)來得到IA的預(yù)編碼矩陣,而且證明了該算法是收斂的。進(jìn)而,分別就權(quán)值為常數(shù)以及權(quán)值可調(diào)情況,討論了各自算法的性能。結(jié)果表明,雖然與Max-SINR算法相比,加權(quán)算法的性能,在低SNR時仍有差距,但較分布式IA,性能有明顯提高,而且權(quán)值可調(diào)算法的性能在低SNR時要明顯優(yōu)于固定權(quán)值算法。由于IA的關(guān)注點在于最大程度地消除干擾對期望信號的影響,因而其在低信噪比時的性能較差。針對這個問題,本學(xué)位論文研究了分集IA的實現(xiàn)方法及性能。首先給出了分集IA的實現(xiàn)條件,然后以接收分集為例研究了單側(cè)分集的實現(xiàn)方法并通過仿真給出了其性能。接著,探索了同時在收發(fā)兩側(cè)同時實現(xiàn)分集的可能性,并提出了實現(xiàn)方案,給出了初步的分析結(jié)果。結(jié)果表明,在同樣的天線配置條件,分集可以有效地提高IA在低信噪比時的性能,而在高信噪比時,復(fù)用的性能要優(yōu)于分集。為了得到IA在復(fù)雜通信系統(tǒng)中的性能,迫切需要得到它的理論近似。而隨機矩陣為物理層提供了良好的理論近似,也適用于干擾信道的研究。本文基于隨機矩陣?yán)碚?給出了IA信道容量的理論預(yù)測。由于IA的核心思想在于消除干擾對期望信號的影響,根據(jù)這個特征,首先用漸近特征分析的方法,得到IA等效矩陣特征值的分布特點,給出了IA理論上的系統(tǒng)性能。然后,又得到了IA系統(tǒng)容量的確定性等價矩陣,并用不動點方程的方法成功對該問題求解。數(shù)值仿真表明,以上兩者都得到正確的預(yù)測結(jié)果,且后者的適用范圍更廣,能夠用于分集干擾對齊的研究,且不要求矩陣的極限存在。
[Abstract]:One of the main objectives of wireless communication is to improve the spectral efficiency of the system, and interference is one of the main limiting factors to achieve this goal. It not only affects the enhancement of spectral efficiency, but also lacks effective processing schemes. Recently, the research on interference processing technology has made great progress, that is, interference alignment (Interference Alignment, IA). Research shows that interference is not the fundamental limitation of wireless communication. In theory, IA can gain K/2 times multiplex gain in the interference channel, in which K is the number of users in the interference channel, that is, the channel capacity of IA can be linear with the number of users, which causes IA to attract extensive attention to.IA through precoding technology, so that in each destination node, The main research contents of this dissertation include four aspects: the closed precoding algorithm of IA, the iterative precoding algorithm, the subset algorithm and the IA performance based on the random matrix theory. The contents are as follows: first, the condition of interference alignment is given, and the non uniqueness of the interference alignment solution is proved. In some cases, the precoding matrix of the interference alignment can be obtained directly, but because the target function of the interference alignment is not the channel capacity, and the multi solution of the IA problem makes the optimization of the IA solution more difficult. In view of this problem, the characteristics of the inner product norm of the expected signal and the interference subspace in IA are analyzed. The relationship between the inner product norm of the two and the signal power in the later stage is obtained. The interference alignment algorithm with the minimum inner product norm is proposed. The analysis shows that the algorithm can optimize the expected signal energy and improve the capacity of the system. The closed precoding matrix to IA can only be obtained in a specific scene. In general, the precoding matrix of IA can only obtain an iterative algorithm of.IA by iterative method. First, a target function is set up, and the precoding matrix is obtained by iterative minimization of the target function. The typical iterative interference alignment algorithm is distributed IA, and the maximum is the largest. The main problem of the Max-Signal-to-Interference-Noise-Ratio (Max-SINR) algorithm, which can be used as the capacity limit of the interference channel to a certain extent, is that it can not guarantee the convergence and the complexity is very high. By studying the reason of the non convergence of the Max-SINR algorithm, and using the idea of the algorithm, the interference subspace is analyzed. The respective characteristics and relations of the subspace of the signal and the expected signal are presented. The weighted sum of the corresponding subspace power of the interference and the expected signal is given as the target function, and the precoding matrix of the IA is obtained by minimizing the objective function iteratively, and it is proved that the algorithm is convergent. Then, the weights are constant and the weights are available, respectively. The performance of each algorithm is discussed. The results show that, although compared with the Max-SINR algorithm, the performance of the weighted algorithm still has a gap at low SNR, but the performance of the distributed IA is obviously improved, and the performance of the weight adjustable algorithm is obviously superior to the fixed weight algorithm when the SNR is low. Because the focus of IA is to eliminate the maximum degree. Interfering with the effect of the expected signal, so its performance is poor at low signal to noise ratio. For this problem, this dissertation studies the implementation and performance of diversity IA. First, we give the realization conditions of diversity IA. Then, taking the reception diversity as an example, we study the implementation of the single side diversity and give its performance through simulation. At the same time, the possibility of realizing diversity at the same time at both sides of the receiver is presented, and the implementation scheme is proposed and the preliminary analysis results are given. The results show that the diversity of the IA can effectively improve the performance of the same antenna configuration when the signal to noise ratio is low, while the performance of the multiplexing is better than the diversity at high signal to noise ratio. In order to get IA in the complex communication system, The performance of the system is urgent to obtain its theoretical approximation. The random matrix provides a good theoretical approximation to the physical layer and is also suitable for the study of the interference channel. Based on the random matrix theory, the theoretical prediction of the capacity of the IA channel is given. Because the core idea of IA is to eliminate the influence of interference to the desired signal, according to this feature First, the characteristic value distribution of the IA equivalent matrix is obtained by the asymptotic characteristic analysis method, and the system performance in the IA theory is given. Then, the deterministic equivalent matrix of the capacity of the IA system is obtained, and the solution of the problem is solved by the fixed point equation. The numerical simulation shows that the above two get the correct prediction results. The latter is more suitable for the study of diversity interference alignment and does not require the existence of matrix limit.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN911.4
，
本文編號：2129520
[Abstract]:One of the main objectives of wireless communication is to improve the spectral efficiency of the system, and interference is one of the main limiting factors to achieve this goal. It not only affects the enhancement of spectral efficiency, but also lacks effective processing schemes. Recently, the research on interference processing technology has made great progress, that is, interference alignment (Interference Alignment, IA). Research shows that interference is not the fundamental limitation of wireless communication. In theory, IA can gain K/2 times multiplex gain in the interference channel, in which K is the number of users in the interference channel, that is, the channel capacity of IA can be linear with the number of users, which causes IA to attract extensive attention to.IA through precoding technology, so that in each destination node, The main research contents of this dissertation include four aspects: the closed precoding algorithm of IA, the iterative precoding algorithm, the subset algorithm and the IA performance based on the random matrix theory. The contents are as follows: first, the condition of interference alignment is given, and the non uniqueness of the interference alignment solution is proved. In some cases, the precoding matrix of the interference alignment can be obtained directly, but because the target function of the interference alignment is not the channel capacity, and the multi solution of the IA problem makes the optimization of the IA solution more difficult. In view of this problem, the characteristics of the inner product norm of the expected signal and the interference subspace in IA are analyzed. The relationship between the inner product norm of the two and the signal power in the later stage is obtained. The interference alignment algorithm with the minimum inner product norm is proposed. The analysis shows that the algorithm can optimize the expected signal energy and improve the capacity of the system. The closed precoding matrix to IA can only be obtained in a specific scene. In general, the precoding matrix of IA can only obtain an iterative algorithm of.IA by iterative method. First, a target function is set up, and the precoding matrix is obtained by iterative minimization of the target function. The typical iterative interference alignment algorithm is distributed IA, and the maximum is the largest. The main problem of the Max-Signal-to-Interference-Noise-Ratio (Max-SINR) algorithm, which can be used as the capacity limit of the interference channel to a certain extent, is that it can not guarantee the convergence and the complexity is very high. By studying the reason of the non convergence of the Max-SINR algorithm, and using the idea of the algorithm, the interference subspace is analyzed. The respective characteristics and relations of the subspace of the signal and the expected signal are presented. The weighted sum of the corresponding subspace power of the interference and the expected signal is given as the target function, and the precoding matrix of the IA is obtained by minimizing the objective function iteratively, and it is proved that the algorithm is convergent. Then, the weights are constant and the weights are available, respectively. The performance of each algorithm is discussed. The results show that, although compared with the Max-SINR algorithm, the performance of the weighted algorithm still has a gap at low SNR, but the performance of the distributed IA is obviously improved, and the performance of the weight adjustable algorithm is obviously superior to the fixed weight algorithm when the SNR is low. Because the focus of IA is to eliminate the maximum degree. Interfering with the effect of the expected signal, so its performance is poor at low signal to noise ratio. For this problem, this dissertation studies the implementation and performance of diversity IA. First, we give the realization conditions of diversity IA. Then, taking the reception diversity as an example, we study the implementation of the single side diversity and give its performance through simulation. At the same time, the possibility of realizing diversity at the same time at both sides of the receiver is presented, and the implementation scheme is proposed and the preliminary analysis results are given. The results show that the diversity of the IA can effectively improve the performance of the same antenna configuration when the signal to noise ratio is low, while the performance of the multiplexing is better than the diversity at high signal to noise ratio. In order to get IA in the complex communication system, The performance of the system is urgent to obtain its theoretical approximation. The random matrix provides a good theoretical approximation to the physical layer and is also suitable for the study of the interference channel. Based on the random matrix theory, the theoretical prediction of the capacity of the IA channel is given. Because the core idea of IA is to eliminate the influence of interference to the desired signal, according to this feature First, the characteristic value distribution of the IA equivalent matrix is obtained by the asymptotic characteristic analysis method, and the system performance in the IA theory is given. Then, the deterministic equivalent matrix of the capacity of the IA system is obtained, and the solution of the problem is solved by the fixed point equation. The numerical simulation shows that the above two get the correct prediction results. The latter is more suitable for the study of diversity interference alignment and does not require the existence of matrix limit.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN911.4
，
本文編號：2129520
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