Doubly-Selective Channel Estimation Using Superimposed Training and Weighted First-Order Statistics

Doubly-selective channel estimation using superimposed training and complex exponential basis expansion model is considered. By taking a weighted averaging operation of the received data, a weighted first-order statistical estimator is proposed, where the time-varying channel estimation is reduced to the simple average-based solution of time-invariant coefficients and the dominant effect of information-induced interference on channel estimation can be suppressed. To further improve the estimation performance with a limited training power, a joint iterative channel estimation and symbol detection scheme is developed where the detected symbol is exploited to enhance estimation performance instead of being viewed as interference. Theoretical analysis and simulation results show that the proposed scheme is superior to data-dependent superimposed training scheme and competitive with the conventional time-multiplexed training in terms of symbol error rate over doubly-selective channels.     

Full-Hardware Architectures for Data-Dependent Superimposed Training Channel Estimation

Channel estimation based on superimposed training (ST) has been an active research topic around the world in recent years, because it offers similar performance when compared to methods based on pilot assisted transmissions (PAT), with the advantage of a better bandwidth utilization. However, physical implementations of such estimators are still under research, and only few approaches have been reported to date. This is due to the computational burden and complexity involved in the algorithms in conjunction with their relative novelty. In order to determine the suitability of the ST-based channel estimation for commercial applications, the performance and complexity analysis of the ST approaches is mandatory. This work proposes two full-hardware channel estimator architectures for a data-dependent superimposed training (DDST) receiver with perfect synchronization and nonexistent DC-offset. These architectures were described using Verilog HDL and targeted in Xilinx Virtex-5 XC5VLX110T FPGA. The synthesis results of such estimators showed a consumption of 3 % and 1 % of total slices available in the FPGA and frequencies operation over 160 MHz. They have also been implemented on a generic 90 nm CMOS process achieving clock frequencies of 187 MHz and 247 MHz while consuming 3.7 mW and 2.74 mW, respectively. In addition, for the first time, a novel architecture that includes channel estimation, training/block synchronization and DC-offset estimation is also proposed. Its fixed-point analysis has been carried out, allowing the design to produce practically equal performance to those achieved with the floating-point models. Finally, the high throughputs and reduced hardware consumptions of the implemented channel estimators, leads to the conclusion that ST/DDST can be utilized in practical communications systems.     

基于数据依赖的叠加训练序列信道估计

通过采用依赖于信息数据的叠加式周期训练序列发送机制,考虑到信道存在未知直流偏移的情况,提出了一种估计频率选择性信道的新算法。推导出信道估计的均方误差公式,获得了最优的训练序列所应满足的条件。理论分析和仿真结果表明,该算法大大提高了信道估计和信号检测的性能,从而验证了该算法的有效性和可靠性。     

Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

In this letter we propose, for the first time, a solution to the problem of carrier frequency offset (CFO) estimation within the data dependent superimposed training (DDST) framework for channel estimation. While time division multiplexed (TDM) trained systems can use the TDM sequence to determine the CFO, the original attraction of DDST for channel estimation was that it avoided any TDM training. So in this letter we show how CFO estimation can still be very effectively performed with the DDST algorithm, while continuing to preclude the need for any additional bandwidth-consuming TDM training. Finally, simulations are presented that verify the theoretical results.     

基于叠加训练序列的MIMO信道辨识

信道状态信息(CSI)对于无线通信来说至关重要,而叠加训练序列的信道估计方法由于不占用额外的信号带宽和具有较高的估计精度而受到人们的注意。从接收信号的一阶统计量入手,并且在训练序列为PN序列下,利用循环To-eplitz矩阵的特性,得到了信道估计的算法。仿真实验的结果表明,这种算法不需要矩阵求逆、计算量小,估计精度高,有很大的实际应用前景。     

一种OFDM系统中叠加导频的信道估计方法

提出了一种新的基于叠加导频的信道估计算法。利用循环序列频域能量集中在某些频点上的特点,消除未知传输数据对导频的影响,将导频完全从接收数据中分离出来。在此基础之上,结合PN序列的自相关特性,在时域进行信道估计,进一步降低了噪声对导频的影响。仿真结果表明,与传统方法相比,该方法不但误码率和均方误差更低,而且还具有计算复杂度低、频带利用率高的特点。     

叠加弱能量训练序列信道估计方法

本文提出了一种利用叠加弱能量的周期训练序列进行信道估计的线性最小均方误差(LMMSEE)算法.该方法不需要信道先验信息、不占用宝贵的带宽资源、计算量比常规LS方法更低.理论分析和计算机仿真表明:在训练序列周期比信道冲击响应长度大时,在较低的信噪比下,利用LMMSEE方法估计信道性能比LS方法更佳.     

基于BEM的非平稳双选信道估计方法

面向高速环境下的无线通信系统,针对高速信道的双选衰落和非平稳特性,提出一种基于基扩展模型（Basis Expansion Model,BEM）的贝叶斯滤波的信道估计方法.针对双选衰落特性,采用BEM信道模型,降低估计复杂度,消除子载波间干扰;针对非平稳特性,提出一种基于贝叶斯滤波的联合估计信道冲激响应与时变的时域自相关系数的信道估计方法.仿真分析表明,所提方法相较最小二乘法等传统方法在高速环境下能够提升估计精度和误码率性能.本方法特别适用于高速铁路的无线通信系统.     

叠加训练序列的判决指导信道估计方法

针对实际应用中信道先验信息未知时无法求解信道相关矩阵的问题,提出了一种改进的叠加训练序列的判决指导信道估计方法。该方法利用每次迭代的信道估计值求解信道相关矩阵。推导了该方法中信道相关矩阵的求解公式。仿真结果表明,在未知信道先验信息的情况下,与利用信道先验信息的叠加训练序列的信道估计方法相比,改进方法取得了与之几乎一样的性能,因此改进方法更适用于实际应用。     

Time-varying Channel Estimation and Symbol Detection Using Superimposed Training in OFDM Systems

In mobile orthogonal frequency division multiplexing (OFDM) systems, time-varying channels result in severe intercarrier interference (ICI), and greatly degrade the system performance. So, it is necessary to estimate the accurate channel for equalization of received symbols. But, the conventional pilot-assisted channel estimation scheme consumes valuable bandwidth. In this paper, we adopt superimposed training approach for OFDM systems to estimate the time-varying channel, which is approximated by a basis expansion model (BEM). The proposed scheme is an extension of the superimposed training approach previously proposed for time-invariant channels in OFDM systems. At the same time, we employ an iterative best linear unbiased estimator (BLUE) to minimize the mean square error (MSE) of the coefficient estimates and improve the system performance. Simulation results prove the effectiveness of the proposed scheme in fast time-varying scenario.

叠加训练序列OFDM系统的一种迭代信道估计方法

在瑞利多径衰落信道中，针对叠加训练序列正交频分复用系统的迭代接收机，提出了一种迭代信道估计方法．在迭代译码开始前，利用与数据符号同时发送的训序列得到信道估计初始值，并在每次迭代后利用更新的码字比特先验信息估计接收信号中的数据符号，计算更准确的信道估计值．理论分析给出了每次迭代后的信道估计值的均方误差性能．在多径衰落信道中，通过计算机仿真验证了该迭代信道估计方法的有效性和可靠性。     

BICM-OFDM中利用叠加导频的迭代信道估计

提出了一种利用叠加导频的迭代信道估计算法。将导频信号叠加在发送信号上,利用一阶统计量进行初始信道估计。利用比特交织编码调制（BICM）中迭代译码的硬判决反馈信息,进行迭代信道估计。该算法在不牺牲信息速率的前提下,有效提高了系统带宽利用率。在BICM-OFDM系统中的仿真结果表明,与传统叠加导频算法相比,文章提出的算法可有效提高信道估计精度和系统性能。     

基于部分数据的叠加序列慢时变信道估计

针对OFDMA通信系统,提出了一种基于部分数据的叠加序列慢时变信道估计算法,并在接收端给出了数据恢复的方法。时变信道采用复指数基扩展模型来描述,对OFDMA系统的导频序列进行了精心设计。提出在频域减去一个基于部分数据的序列,从而使发送数据经过反傅里叶变换后,特定位上的数据能量变低,进而大大减少了数据在信道估计时产生的误差。由于在发射端信号已经产生了畸变,在接收端采用特定的方法对数据进行补偿,消除了这种影响。实验仿真证明该方法在一定程度上提高信道估计的性能。     

IDMA系统中基于序列叠加信道估计的功率分配方法

本文在序列叠加信道估计研究的基础上,以交织多址接入系统为模型,利用导频训练序列与信息序列不相关的特性在接收端估计出信道状态信息,并采用最大化有效信噪比的方法给出了训练序列与信息序列的最优功率分配策略.此外,本文讨论了不同统计特性的训练序列对信道估计性能的影响以及估计误差对信道容量的影响,通过仿真研究验证了相关的理论分析,并说明了本文功率分配算法较其它分配策略更有利于提高系统性能.     

选择性叠加导频信道估计

叠加导频信道估计方法是将导频与信息数据直接叠加,不占用专门的频谱和时隙,估计算法灵活多样,能解决导频信道估计系统中频谱利用率低和盲估计中不能实时估计的问题。介绍了叠加导频的MIMO-OFDM系统,采用正交多相序列作为叠加导频,利用导频符号的自相关及导频和接收信号的互相关,提出了一种选择性叠加导频信道估计信道的算法。最后,通过理论分析和计算机仿真,表明该方法在快衰落环境中有良好的性能。     

基于叠加训练序列的低复杂度时变信道估计方案

与传统的时分/频分复用训练序列相比,采用叠加训练序列的传输方案可以有效地提高系统的频谱利用率。然而,叠加方案中训练序列与信息序列的相互干扰会造成系统性能的严重下降,如何有效消除信息干扰是提高信道估计性能的关键。该文针对时变衰落信道,首先提出一种新的基于一阶统计量信道估计算法。该算法利用基扩展模型(BEM)构建时变信道,通过时域分块平均的方法来抑制信息序列干扰。在此基础上,利用信息序列和训练序列经历相同信道衰落的特性,提出一种基于加权最小二乘(WLS)的迭代信道估计与检测方案。新方案利用 Kalman滤波检测器代替确定性最大似然(DML)检测器,将检测符号序列看作附加的“训练序列”用于信道估计,从而可以显著提高信道估计性能。仿真结果表明,新方案可以有效消除信息序列干扰,且性能和计算复杂度均优于现有的同类方案。     

基于隐训练序列的MIMO信道估计

提出一种基于隐训练序列的MIMO信道估计模型,模型信道估计方法不占用额外的信号带宽和具有较高的估计精度.采用最小二乘(LS)方法,推导出信道估计误差的均方差和信道容量的下限.数值模拟结果证实在相同条件下,采用隐训练序列要比直接采用训练序列对系统容量的改善5 dB左右,并且也验证一些有用的结论:不恰当的天线数目不能提高信道容量.     

菱形中继网络中基于叠加训练的信道估计技术研究

针对放大转发(Amplify-and-Forward, AF)模式下的菱形中继网络,为了高效获取级联和单跳链路信道状态信息(Channel State Information, CSI),本文提出基于叠加训练的信道估计方案,以消除多址接入干扰和训练间互干扰为目标,进行最优的多训练序列设计。新方案将中继训练叠加到源训练序列上,通过对中继识别符号以及中继训练组进行联合优化设计,设计了一种基于频域循环移位的正交扩展序列组生成算法。为了消除非高斯复合噪声对单跳信道估计造成的严重干扰,提出了一种中继噪声消除算法。通过两路中继链路获取的信息副本,能够在端节点实现分集合并,有效提高符号检测性能。仿真实验对比了同类型的信道估计方案,分析验证了方案的有效性。      

OFDM叠加导频联合信道估计和检测方法

针对现有正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)系统信道估计和迭代检测算法中频谱效率低和鲁棒性差等问题,提出了一种基于酉近似消息传递和叠加导频的信道估计与联合检测方法。首先,在软调制/解调中叠加导频对正交幅度调制的星座点进行预处理,检测时将叠加的导频作为频域符号的先验分布,利用置信传播算法进行调制和解调,实现检测模型的简化。然后,应用因子图-消息传递算法对OFDM传输系统和信道进行建模和全局优化,引入酉变换加强信道估计算法的鲁棒性。最后,建立OFDM仿真环境对现有方法进行仿真分析。仿真结果表明,相对于现有的独立导频类算法,所提算法能够以相同复杂度显著提升OFDM系统的频谱效率和鲁棒性。     

Time‐Varying Multipath Channel Estimation with Superimposed Training in CP‐OFDM Systems

Based on superimposed training methods, a novel time‐varying multipath channel estimation scheme is proposed for orthogonal frequency division multiplexing systems. We first develop a linear least square channel estimator, and meanwhile find the optimal superimposed sequences with respect to the channel estimates’ mean square error. Next, a low‐rank approximated channel estimator is obtained by using the singular value decomposition. As demonstrated in simulations, the proposed scheme achieves not only better performance but also higher bandwidth efficiency than the conventional pilot‐aided approach.