基于时域训练序列的MIMO系统信道估计算法研究

本文介绍了MIMO系统使用训练序列进行信道估计的方法。描述了基于时域训练序列的信道估计算法及采用遗传算法优化后的训练序列的信道估计算法。理论分析和仿真结果显示,后者性能更好,估计误差低于前者。     

基于叠加训练序列的MIMO-OFDM信道估计

为能够估计出MIMO-OFDM系统的信道状态信息(CSI)又能充分利用系统频谱资源,提出了一种基于叠加训练序列的信道估计算法。该算法即将非随机训练序列叠加于信息序列之上,利用训练序列与信息序列的不相关特性,在没有带宽损失的情况下估计出信道参数。并对估计的均方误差性能进行了分析,讨论了训练序列的优化方案。仿真结果证明了该方法的有效性。     

基于隐训练序列的MIMO通信系统信道估计与跟踪

针对MIMO通信系统的信道估计与跟踪问题,提出了一种基于隐训练序列（ITS）的信道估计算法,分析了该算法的均方误差性能,给出了训练序列的优化方案。仿真表明,该算法与传统的最小二乘信道估计算法、预编码隐训练序列算法相比,具有估计精度高、计算量低、易于优化训练序列等特点,且算法不受接收端存在直流偏移的影响,其自适应结构能够很好地实现对快时变通信信道的跟踪,对解决电子战中快时变通信信道的捕获和跟踪问题具有一定的指导意义和应用价值。     

基于隐训练序列的MIMO通信系统信道估计与跟踪

针对MIMO通信系统的信道估计与跟踪问题,提出了一种基于隐训练序列(ITS)的信道估计算法,分析了该算法的均方误差性能,给出了训练序列的优化方案.仿真表明,该算法与传统的最小二乘信道估计算法、预编码隐训练序列算法相比,具有估计精度高、计算量低、易于优化训练序列等特点,且算法不受接收端存在直流偏移的影响,其自适应结构能够很好地实现对快时变通信信道的跟踪,对解决电子战中快时变通信信道的捕获和跟踪问题具有一定的指导意义和应用价值.     

一种适用于终端移动的OFDM无线局域网的信道估计方法

本文提出了一种适用于具有移动终端的OFDM无线局域网的信道估计方法.该方法采用了卡尔曼滤波算法进行了信道估计,并利用导频进行信道跟踪.将基于训练序列的信道估计结果作为Kalman滤波器的初始值和观测值,用基于导频的信道估计结果来计算Kalman滤波器参数.并利用导频进一步跟踪信道在时间上的变化.Simulink仿真结果表明,该算法比基于导频的信道估计方法和基于训练序列的信道估计方法效果都要好.     

一种基于期望最大化的OFDM半盲信道估计算法

提出了一种OFDM半盲信道估计算法。传统的OFDM系统信道估计是依赖于训练序列，但是在无线通信系统中，运用半盲信道估计的性能和灵活性更好。在无线局域网的5GHz频宽的OFDM背景下，利用EM算法仿真，结果表明该算法的SNR与传统训练序列信道估计比较提高了2dB。     

一种基于直接判决的OFDM快衰落信道估计新方法

为了解决正交频分复用（Orthogonal Frequency-Division Multiplexing，OFDM）无线局域网信道估计和跟踪问题，采用直接判决算法进行信道估计，并从中选择可靠的估计结果，结合导频信号进行信道跟踪。将基于训练序列的信道估计结果作为直接判决算法的初始值，利用传输信号直接判决的统计特性进行信道估计，并利用改进的导频算法进一步跟踪信道在时间上的变化。Simulink仿真结果表明，该算法优于基于导频的信道估计方法和基于训练序列的信道估计方法。     

多中继协同通信系统中基于循环正交训练序列的信道估计算法

提出了一种实用的基于循环正交序列的信道估计算法。源节点发送循环正交的信道训练序列,各个中继节点对接收到的信道训练序列进行不同间隔的循环移位后向目的节点转发。尽管各个中继转发的序列在时间上是叠加的,但序列之间不存在相关性。由此可以推导出相应的最小二乘信道估计和线性最小均方误差信道估计。仿真结果证明该算法具有较高的估计精度和效率,同时运算复杂度较低。     

基于自适应压缩感知的信道估计算法

传统方法压缩感知算法截取训练序列最后未被数据干扰固定部分作为观测矩阵,该方法为了抵抗最差的信道而浪费了大量的可用观测数据。在此基础上提出了一种自适应压缩感知的信道估计算法,首先对训练序列进行自适应检测,得到整个未受干扰的观测矩阵,再用压缩感知算法计算信道估计。仿真结果表明,这种基于自适应压缩感知的信道估计算法大幅提高了信道估计的准确性。     

隐含训练序列信道估计中的功率分配

与传统时分发送训练序列的信道估计算法相比,隐含训练序列信道估计算法将训练序列与信息序列直接相加后通过天线发送,从而节约了信道带宽。然而,在天线发送总功率一定时,训练序列的功率越大,信息序列的功率便越小,从而导致信道均衡器的信噪比减小。本文研究了基于MIMO系统的隐含训练序列信道估计算法,分析了信道均衡器信噪比与训练序列功率的关系,并根据均衡器信噪比最大原则推导出训练序列与信息序列的最佳功率分配。分析和仿真结果表明:在训练序列的最佳功率点上,信道均衡器的信噪比最高;随着接收天线信噪比的增加,训练序列的最佳功率增大。     

Channel estimation using implicit training

In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance.     

Training Signal Design for Estimation of Correlated MIMO Channels With Colored Interference

In this paper, we study the problem of estimating correlated multiple-input multiple-output (MIMO) channels in the presence of colored interference. The linear minimum mean square error (MMSE) channel estimator is derived and the optimal training sequences are designed based on the MSE of channel estimation. We propose an algorithm to estimate the long-term channel statistics needed for the construction of the optimal training sequences. We also design an efficient scheme to feed back the required information to the transmitter where we can approximately construct the optimal sequences. Numerical results show that the optimal training sequences provide substantial performance gain for channel estimation when compared with other training sequences     

高速移动环境下基于深度学习的信道估计方法

针对高速移动环境下信道快时变、非平稳特性导致下行链路信道估计性能受限的问题,本文提出一种基于深度学习的信道估计网络,即ChanEstNet.ChanEstNet使用卷积神经网络（Convolutional Neural Network,CNN）提取信道响应特征矢量和循环神经网络（Recurrent Neural Network,RNN）进行信道估计.我们利用标准的高速信道数据对学习网络进行离线训练,充分挖掘训练样本中的信道信息,使其学习到高速移动环境下信道快时变和非平稳的特点,更好的跟踪高速环境下信道的变化特征.仿真结果表明,在高速移动环境下,与传统方法相比,所提信道估计方法计算复杂度低,性能提升明显.     

Power allocation of data dependent superimposed training

Data Dependent Superimposed Training （DDST） scheme outperforms the traditional superimposed training by fully canceling the effects of unknown data in channel estimator. In DDST, however, the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence, which is the motivation of this research. In general, for DDST, there is a tradeoff between the channel estimation accuracy and the data detection reliability, i.e., the more accurate the channel estimation, the more reliable the data detection; on the other hand, the more accurate the channel estimation, the more demanding on the power consumption of training sequence, which in turn leads to the less reliable data detection. In this paper, the relationship between the Signal-to-Noise Ratio （SNR） of the data detector and the training sequence power is analyzed. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector. Analysis and simulation results show that for a fixed transmit power, the SNR and the Symbol Error Rate （SER） of detector vary nonlinearly with the increasing of training sequence power, and there exists an optimal power ratio which accords with the derived optimal power ratio, among the data and training sequence.     

MIMO-OFDM系统的一种改进型LS信道估计算法

基于多输入多输出正交频分复用(MIMO-OFDM)系统,研究了LS信道估计算法,提出了一种训练序列和块状导频联合LS信道估计算法,给出了权值的计算公式.理论推导和计算机仿真表明,这种改进型的LS信道估计方法提高了LS信道估计的误码率性能,并具有较高的灵活性和较低的复杂度增长.     

Least Square Channel Estimation for Two‐Way Relay MIMO OFDM Systems

This letter considers the channel estimation for two‐way relay MIMO OFDM systems. A least square (LS) channel estimation algorithm under block‐based training is proposed. The mean square error (MSE) of the LS channel estimate is computed, and the optimal training sequences with respect to this MSE are derived. Some numerical examples are presented to evaluate the performance of the proposed channel estimation method.     

Optimal periodic training signal for frequency offset estimation in frequency-selective fading channels

This paper addresses an optimal periodic training signal design for frequency offset estimation in frequency-selective multipath Rayleigh fading channels. For a fixed transmitted training signal energy within a fixed-length block, the optimal periodic training signal structure (the optimal locations of identical training subblocks) and the optimal training subblock signal are presented. The optimality is based on the minimum Cramer-Rao bound (CRB) criterion. Based on the CRB for joint estimation of frequency offset and channel, the optimal periodic training structure (optimality only in frequency offset estimation, not necessarily in joint frequency offset and channel estimation) is derived. The optimal training subblock signal is obtained by using the average CRB (averaged over the channel fading) and the received training signal statistics. A robust training structure design is also presented in order to reduce the occurrence of outliers at low signal-to-noise ratio values. The proposed training structures and subblock signals achieve substantial performance improvement.     

MIMO信道估计在对流层散射中的性能分析

将MIMO与对流层散射通信相结合。首先阐述了对流层散射信道的衰落特性,建立了MIMO对流层散射信道的信道模型,并给出了具体的仿真步骤。然后,根据散射信道的特性研究了MIMO基于训练序列的信道估计,重点分析了最小二乘(LS)和最小均方误差(MMSE)估计算法,并针对不同算法的性能进行了仿真分析比较。结果表明,在对流层散射通信中,良好的信道估计能使发送数据在接收端被正确地恢复接收,提高了系统性能。     

Maximum-likelihood-based multipath channel estimation forcode-division multiple-access systems

The problem of estimating the channel parameters of a new user in a multiuser code-division multiple-access (CDMA) communication system is addressed. It is assumed that the new user transmits training data over a slowly fading multipath channel. The proposed algorithm is based on maximum-likelihood estimation of the channel parameters. First, an asymptotic expression for the likelihood function of channel parameters is derived and a re-parametrization of this likelihood function is proposed. In this re-parametrization, the channel parameters are combined into a discrete time channel filter of symbol period length. Then, expectation-maximization algorithm and alternating projection algorithm-based techniques are considered to extract channel parameters from the estimated discrete channel filter, to maximize the derived asymptotic likelihood function. The performance of the proposed algorithms is evaluated through simulation studies. In addition, the proposed algorithms are compared to previously suggested subspace techniques for multipath channel estimation