時(shí)變信道分布式MIMO系統(tǒng)多信道參數(shù)估計(jì)和補(bǔ)償技術(shù)
發(fā)布時(shí)間：2019-05-18 13:06
【摘要】：作為長(zhǎng)期演進(jìn)計(jì)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(duì)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(kuò)展的優(yōu)勢(shì)受到了廣泛的關(guān)注和研究,成為未來(lái)移動(dòng)通信的關(guān)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(dǎo)致各收發(fā)天線對(duì)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(shí)變條件下的移動(dòng)通信技術(shù),因此研究分布式MIMO系統(tǒng)的多頻偏估計(jì)問(wèn)題并將這一問(wèn)題擴(kuò)展到時(shí)變條件下進(jìn)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(jiǎn)單有效的補(bǔ)償頻偏,而分布式MIMO系統(tǒng)中發(fā)端天線存在多個(gè)不同頻偏使得接收端難以對(duì)頻偏進(jìn)行最優(yōu)補(bǔ)償,因此有必要研究基于預(yù)編碼技術(shù)的多頻偏預(yù)糾正方法。本文第二章研究了準(zhǔn)靜態(tài)條件下分布式MIMO系統(tǒng)的多參數(shù)估計(jì)算法。首先,理論推導(dǎo)了分布式MIMO系統(tǒng)中多頻偏估計(jì)的最大似然估計(jì)模型及估計(jì)的克拉美-羅限。然后,推導(dǎo)了基于訓(xùn)練序列相關(guān)的多頻偏估計(jì)算法,針對(duì)此算法存在均方誤差(Mean Square Error,MSE)平臺(tái)的缺點(diǎn),分析并推導(dǎo)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(jì)算法。最后,通過(guò)仿真分析了基于訓(xùn)練序列相關(guān)的估計(jì)算法和EM類算法的性能,并進(jìn)一步對(duì)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(shí)可以較好地克服多天線間干擾,從而較好的克服了MSE平臺(tái)問(wèn)題,取得較好的多參數(shù)估計(jì)性能;相對(duì)于ECM算法,SAGE算法可以較快的達(dá)到收斂。本文第三章研究了時(shí)變條件下分布式MIMO系統(tǒng)的多參數(shù)估計(jì)算法。首先,研究了時(shí)變條件下分布式MIMO系統(tǒng)中多頻偏估計(jì)的最大似然估計(jì)模型。其次,將ECM迭代算法推廣到時(shí)變條件下的多頻偏估計(jì)中,并完成理論推導(dǎo)。然后,針對(duì)ECM算法收斂速度慢的缺點(diǎn),推導(dǎo)出時(shí)變信道條件下SAGE算法,該算法將噪聲與隱藏?cái)?shù)據(jù)空間關(guān)聯(lián)以減少數(shù)據(jù)空間的費(fèi)歇爾信息從而提高收斂速度,完成理論推導(dǎo)。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(shí)變性,取得較好的多參數(shù)估計(jì)性能。本文第四章研究了時(shí)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(yù)校準(zhǔn)技術(shù)。首先,研究了基于預(yù)編碼的多頻偏糾正技術(shù)。其次,在研究單天線校準(zhǔn)技術(shù)的基礎(chǔ)上研究了時(shí)變條件下的多天線校準(zhǔn)模型及校準(zhǔn)過(guò)程。然后,針對(duì)單獨(dú)進(jìn)行多頻偏預(yù)糾正或天線校準(zhǔn)都需要反饋信息的問(wèn)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(duì)此等效信道進(jìn)行天線校準(zhǔn),從而可以在不增加反饋量的條件下同時(shí)實(shí)現(xiàn)多頻偏預(yù)糾正和天線校準(zhǔn)。理論推導(dǎo)和仿真結(jié)果證明,多頻偏和天線增益的聯(lián)合預(yù)校準(zhǔn)技術(shù)可以在不增加反饋量的條件下同時(shí)實(shí)現(xiàn)多頻偏預(yù)糾正和天線校準(zhǔn),取得較好的系統(tǒng)性能。綜上,時(shí)變信道中多參數(shù)估計(jì)和補(bǔ)償技術(shù)是分布式MIMO系統(tǒng)的關(guān)鍵技術(shù)之一,本文對(duì)分布式MIMO系統(tǒng)的多參數(shù)估計(jì)作了較深入的研究,并研究了聯(lián)合多頻偏和天線增益預(yù)校準(zhǔn)的技術(shù),具有一定的理論研究和實(shí)際應(yīng)用價(jià)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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN919.3
[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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN919.3
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