突发通信中基于叠加训练的信道估计方案

针对无线突发通信中频带利用率低、信道参数获取困难等问题,提出了一种应用于突发通信的叠加训练信道估计与检测方案。该方案将信息和训练叠加发送,通过预失真发送信息符号使得训练与信息在频域正交,收端采用一阶统计信道估计和最大似然符号检测,并设计了抗直流干扰的信道估计方案。仿真表明,新方案在消除训练序列的频带开销的情况下获得了较好的信道估计和符号检测性能,与采用时分复用训练的方案相比,其有效吞吐率更优。     

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DDST频域能量最大化的频偏估计方法

数据依赖叠加训练序列(Data-Dependent Superimposed Training,DDST)常用在正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)的信道估计中,由叠加训练序列和数据依赖序列组成,与信息序列并行发送,可以提高带宽利用率。提出了一种基于数据依赖叠加训练序列的OFDM载波频偏估计方法。叠加训练序列的周期性使其DFT能量间隔分布在特定的频点上,利用这个特性可进行频偏估计,只有得到正确的频偏估计时,这些特定频点的能量才得到最大值。仿真表明该方法在不降低传输速率的情况下,有着较好的频偏估计性能。     

基于叠加训练的非合作多用户/MIMO信道估计

提出一种基于叠加训练的单载波非合作多用户/MIMO系统的迭代信道估计与检测方案。首先利用变换域方法构造具有零周期互相关特性的训练序列,从而消除多天线间的相互干扰,实现基于一阶统计量的信道估计。然后采用联合符号检测的迭代信道估计方法,利用检测序列作为额外的“训练序列”来降低信息序列自身干扰。与现有的叠加训练信道估计方案比较,新方案中训练序列构造更加灵活,在低信噪比下信道估计均方误差和误码率性能更优,且复杂度更低,仿真结果表明了该方案的有效性。     

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。      

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计。基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性。     

一种利用叠加训练序列降低OFDM系统PAR的方法

峰均比(PAR)过高是正交频分复用(OFDM)技术亟待解决的关键问题之一。在基于判决指导信道估计的叠加训练序列OFDM系统中,提出一种选择性叠加训练序列降低系统PAR的方法。该方法使用多个相互独立的训练序列分别与数据序列叠加,选择其中PAR性能最好的用于传输,接收端完成信道估计的同时消除了训练序列对数据序列的影响,因此没有增加接收端的复杂度。计算机仿真结果表明:该方法在不增加接收端复杂度的前提下,有效降低了系统PAR,且对信道估计性能不造成任何影响,因此该方法可用于实际系统中。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法.基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计.基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能.进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性.     

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。     

基于隐含ZCZ训练序列的MIMO信道估计性能改进

 本文研究了采用隐含训练序列无直流偏移影响的MIMO频率选择性信道的估计问题.通过选用具有平衡特性的二进制ZCZ序列作为训练序列,可以在不增加任何复杂度的情况下直接消除直流偏移量的影响.由于每根天线的发射数据经过预处理后再算术叠加到训练序列上,从而消除了传统隐含估计方法中未知数据对信道估计性能的影响.本文推导了采用新方法的MIMO信道估计误差方差表达式,并从时域的角度给出了分析.理论分析和仿真结果表明,本文方法性能优于已有的隐含信道估计方法.     

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.     

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.     

On the performance of data-dependent superimposed training without Cyclic Prefix for SISO/MIMO systems

Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for th...     

Optimal insertion of pilot symbols for transmissions over time-varying flat fading channels

Two major training techniques for wireless channels are time-division multiplexed (TDM) training and superimposed training. For the TDM schemes with regular periodic placements (RPPs), the closed-form expression for the steady-state minimum mean square error (MMSE) of the channel estimate is obtained as a function of placement for Gauss-Markov flat fading channels. We then show that among all periodic placements, the single pilot RPP scheme (RPP-1) minimizes the maximum steady-state channel MMSE. For binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) signaling, we further show that the optimal placement that minimizes the maximum uncoded bit error rate (BER) is also RPP-1. We next compare the MMSE and BER performance under the superimposed training scheme with those under the optimal TDM scheme. It is shown that while the RPP-1 scheme performs better at high SNR and for slowly varying channels, the superimposed scheme outperforms RPP-1 in the other regimes. This demonstrates the potential for using superimposed training in relatively fast time-varying environments.     

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.     

Orthogonal superimposed training design for block transmission over frequency‐selective fading channels

Channel estimation for single‐carrier block transmission over frequency‐selective fading channel using superimposed training is addressed. A novel affine precoding model based on orthogonal polyphase sequence set is designed to decouple channel estimation from symbol detection. The orthogonal constraints on the training and precoding matrices ensure the separation of superimposed signals and accurate channel estimation with less training overheads as compared with time‐multiplexed scheme. Simulation results show that the proposed scheme exhibits good performance and outperforms another data‐dependent superimposed training scheme, especially for compact constellations or channel with long delay‐spread. Copyright © 2014 John Wiley & Sons, Ltd.     

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。