【摘要】：作為長期演進計劃(Long Term Revolution,LTE)中的核心技術(shù)之一,多輸入多輸出(Multiple Input Multiple Output,MIMO)可以有效的提供分集增益和功率增益。隨著對MIMO技術(shù)研究的深入,分布式MIMO系統(tǒng)以其高容量、廣覆蓋、低功耗、和易擴展的優(yōu)勢受到了廣泛的關注和研究,成為未來移動通信的關鍵技術(shù)之一。在分布式MIMO系統(tǒng)中,由于收發(fā)天線可能分布在不同的地理位置,從而導致各收發(fā)天線對之間的頻偏可能各不相同。高速鐵路的飛速發(fā)展迫切需要適用于時變條件下的移動通信技術(shù),因此研究分布式MIMO系統(tǒng)的多頻偏估計問題并將這一問題擴展到時變條件下進行研究具有較為重要的意義。單輸入單輸出(Single Input Single Output,SISO)系統(tǒng)和集中式MIMO系統(tǒng)在接收端可以簡單有效的補償頻偏,而分布式MIMO系統(tǒng)中發(fā)端天線存在多個不同頻偏使得接收端難以對頻偏進行最優(yōu)補償,因此有必要研究基于預編碼技術(shù)的多頻偏預糾正方法。本文第二章研究了準靜態(tài)條件下分布式MIMO系統(tǒng)的多參數(shù)估計算法。首先,理論推導了分布式MIMO系統(tǒng)中多頻偏估計的最大似然估計模型及估計的克拉美-羅限。然后,推導了基于訓練序列相關的多頻偏估計算法,針對此算法存在均方誤差(Mean Square Error,MSE)平臺的缺點,分析并推導了基于期望最大化(Expectation Maximization,EM)類的迭代多參數(shù)最大似然估計算法。最后,通過仿真分析了基于訓練序列相關的估計算法和EM類算法的性能,并進一步對比了EM類算法中期望最大化(Expectation Conditional Maximization,ECM)算法和空間交替期望最大化(space-Alternating Generalized Expectation-maximization,SAGE)算法的性能。理論分析和仿真結(jié)果證明,基于EM類迭代的最大似然類算法在較高信噪比時可以較好地克服多天線間干擾,從而較好的克服了MSE平臺問題,取得較好的多參數(shù)估計性能;相對于ECM算法,SAGE算法可以較快的達到收斂。本文第三章研究了時變條件下分布式MIMO系統(tǒng)的多參數(shù)估計算法。首先,研究了時變條件下分布式MIMO系統(tǒng)中多頻偏估計的最大似然估計模型。其次,將ECM迭代算法推廣到時變條件下的多頻偏估計中,并完成理論推導。然后,針對ECM算法收斂速度慢的缺點,推導出時變信道條件下SAGE算法,該算法將噪聲與隱藏數(shù)據(jù)空間關聯(lián)以減少數(shù)據(jù)空間的費歇爾信息從而提高收斂速度,完成理論推導。理論分析和仿真結(jié)果證明,本章所提算法可以較好的克服信道的時變性,取得較好的多參數(shù)估計性能。本文第四章研究了時變條件下分布式MIMO系統(tǒng)的多頻偏和天線增益的聯(lián)合預校準技術(shù)。首先,研究了基于預編碼的多頻偏糾正技術(shù)。其次,在研究單天線校準技術(shù)的基礎上研究了時變條件下的多天線校準模型及校準過程。然后,針對單獨進行多頻偏預糾正或天線校準都需要反饋信息的問題,把多頻偏看做等效信道的一部分,使其與信道信息統(tǒng)一成為等效信道,并對此等效信道進行天線校準,從而可以在不增加反饋量的條件下同時實現(xiàn)多頻偏預糾正和天線校準。理論推導和仿真結(jié)果證明,多頻偏和天線增益的聯(lián)合預校準技術(shù)可以在不增加反饋量的條件下同時實現(xiàn)多頻偏預糾正和天線校準,取得較好的系統(tǒng)性能。綜上,時變信道中多參數(shù)估計和補償技術(shù)是分布式MIMO系統(tǒng)的關鍵技術(shù)之一,本文對分布式MIMO系統(tǒng)的多參數(shù)估計作了較深入的研究,并研究了聯(lián)合多頻偏和天線增益預校準的技術(shù),具有一定的理論研究和實際應用價值。
[Abstract]:As one of the core technologies in Long Term Evolution (LTE), multiple-input multiple-output (MIMO) can effectively provide diversity gain and power gain. With the deep research of MIMO technology, the distributed MIMO system has received extensive attention and research with its high capacity, wide coverage, low power consumption and easy expansion, and becomes one of the key technologies for future mobile communication. In a distributed MIMO system, the frequency offset between the transmit and receive antenna pairs may vary due to the fact that the transmit and receive antennas may be distributed in different geographic locations. The rapid development of high-speed railway urgently needs to be applied to the mobile communication technology under time-varying conditions, so it is of great significance to study the multi-frequency offset estimation problem of the distributed MIMO system and to extend the problem to the time-varying conditions. The single-input single-output (SISO) system and the centralized MIMO system can simply and effectively compensate the frequency offset at the receiving end, Therefore, it is necessary to study the multi-frequency offset pre-correction method based on the precoding technique. In the second chapter, the multi-parameter estimation algorithm of the distributed MIMO system under quasi-static condition is studied. First, the maximum likelihood estimation model of multi-frequency offset estimation in a distributed MIMO system and the estimated KRA-Lo limit are derived. Then, a multi-parameter maximum likelihood estimation algorithm based on the expectation maximization (EM) class is derived. Finally, the performance of the estimation algorithm based on the training sequence and the EM class algorithm is analyzed by the simulation, and the performance of the expectation maximization (ECM) algorithm and the space-alternate generation (SAGE) algorithm in the EM algorithm is further compared. The theoretical analysis and simulation results show that the maximum likelihood algorithm based on the EM class iteration can overcome the inter-antenna interference better when the higher signal-to-noise ratio is higher, so that the problem of the MSE platform is better overcome, and the better multi-parameter estimation performance is obtained; and compared with the ECM algorithm, The SAGE algorithm can achieve convergence faster. In the third chapter, the multi-parameter estimation algorithm of the distributed MIMO system under time-varying conditions is studied. First, the maximum likelihood estimation model of multi-frequency offset estimation in a distributed MIMO system under time-varying conditions is studied. Secondly, the ECM iterative algorithm is extended to the multi-frequency offset estimation under time-varying conditions, and the theoretical derivation is completed. Then, for the disadvantage of slow convergence speed of the ECM, the SAGE algorithm under the condition of time-varying channel is derived. The algorithm associates the noise with the hidden data space to reduce the time-varying information of the data space, so as to improve the convergence speed and complete the theoretical deduction. The results of theoretical analysis and simulation show that the algorithm proposed in this chapter can better overcome the time-changing of the channel and achieve better multi-parameter estimation performance. In the fourth chapter, the combined pre-calibration technique for multi-frequency offset and antenna gain of a distributed MIMO system under time-varying conditions is studied. First, a multi-frequency offset correction technique based on pre-coding is studied. Secondly, the multi-antenna calibration model and the calibration process under time-varying conditions are studied on the basis of the single-antenna calibration technology. then, aiming at the problem that the feedback information is needed for the independent multi-frequency offset pre-correction or the antenna calibration, the multi-frequency offset is regarded as a part of the equivalent channel, and the multi-frequency offset is unified with the channel information into an equivalent channel, and the equivalent channel is subjected to antenna calibration, So that the multi-frequency offset pre-correction and the antenna calibration can be realized at the same time without increasing the feedback amount. The theory and simulation results show that the combined pre-calibration technique of the multi-frequency offset and the antenna gain can realize the multi-frequency offset pre-correction and the antenna calibration without increasing the feedback amount, so as to obtain better system performance. In this paper, the multi-parameter estimation and compensation technique in the time-varying channel is one of the key technologies of the distributed MIMO system. The multi-parameter estimation of the distributed MIMO system is studied in this paper, and the technology of the combination of the multi-frequency offset and the antenna gain pre-calibration is also studied. And has certain theoretical research and practical application value.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.3

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