【作者】 赵俊义；

【导师】 贾世楼； 孟维晓；

【作者基本信息】 哈尔滨工业大学 ， 信息与通信工程， 2009， 博士

【摘要】 提高频谱的利用率是下一代无线移动通信的主要目标之一。在带宽和功率受限的无线通信中,多输入多输出(Multi-Input Multi-Output, MIMO)是获得更高的频谱效率最具有潜力的技术。同时正交频分复用(Orthogongal FrequencyDivision Multiplexing, OFDM)技术具有抵抗多径衰落和降低宽带接收机复杂度等优点,MIMO-OFDM被认为是后3G最有潜力的技术之一,能够适应高信道容量、高比特信息速率等宽带多媒体应用的需求。信道估计作为无线通信系统中的一项关键技术,是MIMO-OFDM系统中相干检测和空时解码的必须条件。因此本文以MIMO-OFDM系统的信道估计作为研究的主要内容。高速无线数据传输导致信道呈现出稀疏特性,利用该特性可提高信道估计性能。针对非时变多径稀疏的MIMO-OFDM信道,提出了基于广义Akaike信息论准则的信道估计算法,该算法能够估计出信道的阶数和每条路径的时延,然后将由最小二乘算法估计出的时域信道冲激响应中非传播路径点置0,由此可降低加性白噪声对信道估计的影响,提高信道估计的精度,同时,信道稀疏性越强,性能提高越大。接收终端的快速移动使得无线MIMO-OFDM通信信道具有快速时变特性。信道快速时变会破坏子载波间的正交性引起子载波间干扰,从而使系统性能下降。论文中首先对时变无线信道建立了一个离散长椭球体序列扩展模型,近似时变信道。同时提出了一种不依赖信道统计特性信息的迭代的估计方法,利用均衡输出的结果消除未知信息序列对信道估计的干扰。在该模型的基础上,采用导频序列对时变MIMO-OFDM信道进行估计。信道估计通常使用导频符号辅助方法,这种方法需要增加额外的系统带宽。为提高带宽效率,对叠加训练序列的方法进行了研究,该方法将一低功率周期性的训练序列直接叠加于信息序列,对接收数据求均值,不需要任何额外系统带宽,可估计信道。该方法在接收端由于采用未知信息序列的算术均值代替其统计平均值,使得未知的信息序列对信道估计的性能产生影响。提出了一种迭代叠加训练序列信道估计方法,即把经过频域均衡、译码得到的发送信息序列的估计值和叠加训练序列之和作为新的训练序列,反馈到信道估计器再次估计信道状态信息,通过这种判决反馈的迭代信道估计方法提高信道估计的性能。首先对于非时变SISO-OFDM信道应用该方法进行信道估计,然后基于长椭球序列基扩展模型,将该方法扩展到快速时变的MIMO-OFDM信道估计。在实际的多用户MIMO-OFDM通信环境中存在有色干扰和信道空间相关,导致信道估计性能的降低。本文在发射总功率恒定的条件下,基于信道估计均方误差最小原则,利用统计“注水”方法对发送端的导频进行预编码,可提高信道估计的精度。该方法只需知道信道的相关特性和干扰的统计特性,不必对其频繁更新,且容易获得。理论分析表明,最佳的发射方向由发射信道相关矩阵和干扰协方差矩阵的特征值分解共同决定。更多还原

【Abstract】 One main target of next generation wireless mobile communication is to obtainmore efficiency of spectrum. In wireless communication with the limitation ofbandwidth and power, MIMO is a potential technique to achieve high efficiency ofspectrum, and considering OFDM technology has the property of high tolerance tomulti-path fading and low complexity to receiver desigen, MIMO-OFDM techniquehas been widely regarded as one of the most promising techniques for Beyond 3rdGeneration(B3G) wireless communication, which is efficient for widebandmultimedia transmission because of its high ability of anti-fading, high channelcapacity and high bit-rate data. As a key technique for wireless mobilecommunication, channel estimation is required for coherent detection and space-timedecoding in MIMO-OFDM systems. So channel estimation for MIMO-OFDM systemis the main research content in this dissertation.The wireless transmission channel with high rates has propertyof sparsitywhichcan be used to improve the performance of channel estimation. For time-invariantsparse MIMO-OFDM channel, a channel estimation algorithm based on generalizedAkaike information criterion(GAIC) is proposed, the channel order and the timedelays of each path can be identified by the algorithm, then the tap ofnon-dissemination path in the time-domain impuse reponse which is estimated byleast square (LS) algorithm is set tozero. Bythis method, performance loss caused byAWGN can be reduced and the channel estimation precision can be improved,meanwhile, as the number of channel path decreases, the proposed algorithmperforms better.Wireless MIMO-OFDM communication channel has property of fasttime-varying due to the high mobility of terminal. The time-selective fading channeldestroys the orthogonality among different subcarriers and cause Inter-CarrierInterference (ICI), then, sytem performance will decrease. A discrete prolatespheroidal sequence based expansion model is established for time-vaying channel,and an iterative channel estimation method that isn’t depended on the statisticalinformation of CSI is presented. To cancel the interference from the unkowninformation, the equalized symbol is reused in channel estimation. Then the fasttime-varying MIMO-OFDM channel is estimated using pilot sequence by the abovemethod.Pilot-symbol assisted modulation (PSAM) scheme is usually adopted by channelestimation, but this scheme requires extra system bandwidth. To improve efficiencyof bandwidth, a superimposed training (ST) based channel estimation method is researched. It means that a periodic training sequence with low power issuperimposed to the information sequence at transmitter, with the mean of receiceddata, channel parameters can be estimated without consuming any extra systembandwidth. The unknown information sequence can be interference to channelestimation due to replacing the statistical mean with arithmetic mean. Based on thisan iterative superimposed training (IST) method is presented. The method uses sumof equalized information sequence and superimposed training as new trainingsequence, which is feedback to channel estimator to estimate channel stateinformation (CSI) again. The channel estimation performance can be improved bythefeedback iterative method. First time-invariant SISO-OFDM channel is estimated bythis method, then the method is extended to fast time-varying MIMO-OFDM channelestimation based on discrete prolate spheroidal sequences based expansion model.There exist colored interference and channel spatial correlation in multi-userMIMO-OFDM system, which result in performance degradation of channelestimation. To improve channel estimation precison, a space-frequency statisticalprecoding scheme is proposed in the dissertation. The precoding scheme is designedat transmitter by utilizing water-filling approach based on the minimum criteria ofchannel estimation mean square error (MSE) under the condition of total powerconstraint. It only requires statistical information of channel correlations and coloredinterference, which need not be updating frequently, and can be easily acquired.Theoretical analysis shows that the optimal transmission directions are determined bythe eigen-decompositions of channel covariance matrices and interference covariancematrices.更多还原