【作者】 王平；

【导师】 范平志；

【作者基本信息】 西南交通大学 ， 交通信息工程及控制， 2010， 博士

【摘要】 基于训练序列的信道估计具有复杂度低,运算速度快,估计精度高等优点,在现代无线通信中占有重要地位。基于训练序列的MIMO(Multiple-Input Multiple-Output)信道估计按训练序列和数据的发送方式可分为三类：时分复用(Time Division Multiplex, TDM),隐含训练序列(Superimposed Training, ST)和数据相关隐含训练序列(Data Dependent Superimposed Training, DDST)信道估计方法。本文围绕如何提升这三种方案的系统性能以及它们的优化性能比较展开研究。论文首先分析了TDM训练序列长度与信道容量的关系；分别推导了训练序列和信息序列功率一定以及峰均功率比给定时,基于频率选择性MIMO信道容量最大的训练序列最优长度；通过仿真分析了训练序列长度对信道容量的影响,最优训练序列长度与信噪比和峰均功率比的关系。然后,论文推导了频率选择性MIMO信道下,ST系统训练序列的最优功率分配。给出了信道均衡器的信噪比与信息序列、训练序列以及噪声功率的关系,并根据此关系推导了ST系统基于均衡器信噪比最大的训练序列和信息序列最优功率的表达式。分析和仿真结果表明,ST训练序列最优功率和接收天线信噪比有关；信号检测误符号率最小值对应的训练序列功率与理论推导的最优功率拟合很好。接着,论文从多个角度研究了提高DDST系统性能的方法。(1)、叠加在训练序列和信息序列上的数据相关序列(Data Dependent Sequences, DDS)对信号检测来说相当于噪声,会严重影响信号估计的性能。论文研究了DDS与信息序列的内在关系,并提出一种既适用于二进制相移键控(BPSK)又适用于高阶幅度调制信号的DDS消除算法。分析了DDST信号检测错误平层产生的原因,推导了误符号率和误码率平层的表达式。为消除DDST信号检测的错误平层,论文又提出一种信号编码算法并给出了该算法数据冗余率的表达式。研究结果表明,与现有算法相比,本文提出的信号检测算法复杂度更低,检测性能更好；信息编码算法以很低的冗余率消除或减小了信号检测错误平层。(2)、为解决DDST系统现有的训练序列与数据帧同步算法都只适用于单入单出(Single-Input Single-Output, SISO)系统,不能直接扩展到MIMO系统的问题,论文提出一种基于平衡零相关区(Zero Correlation Zone, ZCZ)序列的DDST数据帧同步、信道和直流偏置估计联合算法。研究结果表明：本文提出的算法在SISO系统下与已有文献的算法性能接近,在MIMO系统下的性能则比现有算法更佳。(3)、与ST方案类似,当天线发送的总功率一定时,训练序列的功率越大,信号检测的性能越差。本文分析了信道均衡器信噪比和训练序列功率的关系；推导了频率选择性MIMO信道下,不考虑DDS消除和DDS已知时,DDST训练序列和信息序列的最优功率分配并给出了最优功率的表达式。研究结果表明,DDST训练序列的最优功率与信噪比无关,与是否采用DDS消除算法无关与信道增益也无关。论文最后给出了ST和DDST系统训练序列功率最优时,信道容量下界的表达式。论文从训练序列选择,信道估计性能,信道容量以及信号检测误码率和系统吞吐率等方面比较了TDM,ST和DDST的性能。仿真和数值结果分析表明,在训练序列功率和长度都最优,TDM和DDST峰均功率比相等时,DDST除采用现有的DDS消除技术时的信号检测误码率性能比TDM稍差外；其余性能均比TDM好。更多还原

【Abstract】 Channel estimation based on training sequences has the advantage of low complexity, high speed and excellent performance, which plays a very important role in modern wireless communications. From the viewpoint of data and training sequence transmissions, there are three major schemes of training based multi-input multi-output (MIMO) channel estimation. One is time-division multiplexed (TDM) scheme and the other two are superimposed training (ST) and data-dependent superimposed training (DDST) schemes. In this thesis, algorithms to improve the system performance and performance comparison of the three schemes are investigated.First of all, the relationship of channel capacity and training sequence length of TDM is analyzed. Optimal training length of TDM for frequency selected MIMO channel is derived when the power of training and data sequence or peak to average power ratio (PAPR) is given. The effect of training length on channel capacity and the relationship of optimal training length between signal to noise ratio (SNR) and PAPR is analyzed by simulation.Next, the optimal power allocation of ST scheme for frequency selective MIMO channel is derived. The relationship between the SNR of the channel equalizer and the training sequences power is analyzed. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the equalizer. Analysis and simulation results show that the SNR of the channel equalizer is maximized at the optimal training sequence power, and the optimal power of the training sequences is increased with increase of the signal to noise ratio at the received antennas.Then, several algorithms are presented to improve the system performance of DDST. (1). For data detector, the data dependent sequences (DDS) added on the training and data sequences act as noise and thus degrading the data detection performance. A new DDS removal algorithm, which is not only suitable for BPSK signal but also suitable for high order equi-spaced amplitude or equi-spaced square quadrature amplitude modulation (QAM), is presented in this thesis. Symbol and bit error floor of the proposed detection method is analyzed too. To remove the error floor, a data coding method is also proposed and the redundant ratio of the coding algorithm is given. Analysis and simulation results show that the proposed detection method has lower complexity and better performance than the existing methods. The data coding algorithm can remove or reduce the error floor by much lower redundant ratio. (2). The existing DDST block synchronization algorithms work well for Single-input Single-output (SISO) systems, but can hardly work for MIMO system. A new joint block synchronization, channel and dc-offset estimation algorithm based on balanced zero correlation zone (ZCZ) sequence for MIMO system is proposed. Analysis and simulation results show that the new algorithm has the same performance as the existing algorithms for SISO systems when their block and cyclic prefix lengths are the same. While for MIMO systems, the performance of the proposed algorithm is much better than that of the existing algorithms. (3). Similar to the ST scheme, for a fixed transmission power, the data detection performance will degrade with the increase of training power. Relationship between the SNR of the data detector and the training sequence power is analyzed. The optimal power allocation of the training sequences and data sequences is derived when DDS is treated as noise and DDS is known. Analysis and simulation results show that the optimal power of DDST training sequences is independent of SNR and whether the DDS removal algorithm is employed.Finally, the channel capacity lower bounds of ST and DDST schemes are derived when optimal training power is employed. And the performance of TDM, ST and DDST is compared by training sequence selection, channel estimation MSE, data detection BER and system throughput. Simulation and numerical results show that, if the length and power of training is optimal and peak-to-average power ratio (PAPR) of the TDM and DDST is the same, almost all of the above performance of DDST outperforms that of TDM except DDST data detection performance of the existing DDS removal technology.更多还原