一种基于DDST的频域能量最小化OFDM频偏估计方法

提出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,为了减小信息序列对叠加训练序列的影响,引入数据依赖序列使信息序列DFT在这些特定频点上为零,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,这些旋转后的信息序列在这些特定频点的能量才得到最小值。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。     

叠加训练序列OFDM系统的联合频偏与信道估计

频率偏移导致用于叠加训练序列系统中的一阶信道估计方法失效.文中提出一种利用叠加训练序列进行OFDM系统的联合频偏和信道估计的方案.在简单介绍所建立的OFDM系统后,文中利用训练序列的周期性推导出频偏特征向量,并根据阵列信号求子空间的原理,利用接收信号矩阵的奇异值分解求出上述向量所在的线性子空间,通过相关匹配求出频率偏移,最后获得时域信道参数.理论分析表明频偏估计范围反比于训练序列周期,因此算法具有估计范围大的优点,且适用于所有叠加周期训练序列系统.仿真分析表明了算法的有效性.     

叠加共轭对称训练序列的OFDM系统时频同步方法

针对正交频分复用(OFDM)系统对同步错误率和频率偏移敏感的问题,提出了一种叠加共轭对称训练序列的OFDM系统时间和频率同步方法。该同步方法将快速傅里叶逆变换(IFFT)调制后的序列构成具有共轭对称特性的训练序列,并叠加到OFDM数据符号上;在接收端,利用叠加训练序列和融合循环前缀信息来获取OFDM系统同步信息。理论分析和仿真表明:在信噪比为5 dB时,该同步算法定时同步正确概率已接近100%;在信噪比为4 dB时,频偏估计精度可以达到10-4;该同步算法不仅具有更好的同步性能,而且节省系统的有效带宽以及降低系统的计算法复杂度。     

多用户交织OFDM频偏估计

同步技术是OFDM的关键技术之一。提出一种针对多用户交织OFDM载频偏移估计的算法。首先分析上行链路中交织OFDM的信号结构，利用其周期性质导出特定的数据矩阵，实现频偏估计。这种估计方法不需要利用任何训练序列或导频信号，提高了系统频带利用率。仿真表明，该估计算法在信噪比大于零的情况下都能获得较好的频偏估计。     

一种新的OFDM系统载波频偏估计算法

在正交频分复用(OFDM)系统中,频率偏移会破坏子载波间的正交性,降低整个系统的性能。在研究传统频偏估计算法的基础上,利用简单的实数训练序列,进行OFDM系统的载波频偏估计,只需要一个训练序列,就可以有效地在时域内同时对小数倍和整数倍的频率偏移进行估计。仿真结果表明,基于实数训练序列的载波频偏估计算法不仅方法简单,易于实现,而且可提高频偏估计精度。     

基于旋转不变技术实现多用户交织OFDM频偏估计

本文提出一种针对多用户交织OFDM载频偏移估计的算法。首先介绍上行链路中交织OFDM的信号结构,利用其周期性质,形成特定的数据矩阵,利用旋转不变技术(ESPRIT)实现载频偏移估计。这种估计方法不需要利用任何训练序列或导频信号,进一步提高了系统频带利用率,降低了计算复杂度。仿真表明,该估计算法在信噪比大于零的情况下也能获得较好的频偏估计。     

高性能的OFDM频偏估计新方法

频偏估计是OFDM的关键技术.本文提出了一种具有高精度的OFDM频偏估计新方法.新方法首先将接收信号与本地训练序列作相关来消除训练序列的影响,然后进行频率粗同步.补偿粗频偏后再利用消除训练序列影响的序列或循环前缀作相关实现频率精同步.新方法具有频率偏移估计范围大和估计精度高的优点.文中分析并得出这两种估计方法的频偏估计下界.此频偏估计方法使训练序列的设计更加灵活,可按时间同步或信道估计等性能最优来设计.     

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。     

OFDM系统中一种新型联合时频同步算法

OFDM同步技术是OFDM系统中的关键技术。针对OFDM系统面对的大范围频偏问题,提出了一种新的基于单个训练符号进行OFDM同步的算法,构造了新的训练序列以及新的频偏估计函数,该算法能在最大范围内对载波频率偏移做出有效的估计,并且不需要特定结构的训练符号。基于高斯白噪声信道的仿真比较表明了该算法具有较强的优点。     

OFDM系统叠加序列能量分配及同步方案

在OFDM系统中采用叠加训练序列进行同步、信道估计和均衡是一种好方法,但是,叠加训练序列能量分配及同步等问题一直是困扰学术界的难题.本文提出了关于叠加训练序列的一种能量分配方案、两种最佳叠置结构和一种同步算法.理论分析和仿真证明,这些方案和算法对于改善叠加训练序列OFDM系统性能有很大帮助.     

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.     

叠加训练序列设计及其高效传输技术研究

采用训练序列与信息数据叠加的传输方案由于消除训练序列占用的频带开销而受到广泛关注。然而,如何高效的分离叠加信号是实现高效信道估计和可靠检测的基础。通过叠加周期训练序列,研究了基于数据依赖的叠加训练(DDST)方案的高效信道估计和检测方案。并结合信道编码技术,研究了编码条件下DDST方案与传统时分复用(TDM)方案的性能。仿真结果表明,在消除训练带宽开销的情况下,获得DDST方案与TDM的误码率基本保持一致。     

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.     

叠加弱能量序列的OFDM系统时频同步方法

 本文提出一种使用叠加的、弱能量序列进行时间同步和频偏估计的方法,其中所构造的训练序列具有低/零自相关性及弱能量性能,该序列被叠加在信息符号上并仅分配发射机总能量的4.1%;传统利用循环前缀信息估计频偏范围只局限于0.5内,该法使频偏估计范围可达到1,在利用本地序列与接收信号的相关性后可使整个频偏估计范围达到1/4带宽.理论和仿真表明,该法具有比常规算法更优的时间估计精度和频偏估计范围.     

Channel Estimation in OFDM Systems Using a Controlled Superimposition of Training Sequences

This work pertains to the use of superimposed training (ST) for channel estimation in orthogonal frequency division multiplexing (OFDM) based systems. A time domain coherent averaging based channel estimator is derived from the least squares criterion. By exploiting the periodicity of the training sequences in the time domain and inserting zeros instead of data at some of the training sequence subcarrier locations depending on the desired estimation accuracy, a controlled superimposition technique is proposed. This method includes the flexibility to trade off between bandwidth efficiency and performance. The mean squared estimation error (MSEE) performance of such a system is mathematically analyzed and a training sequence selection criterion optimizing the same is proposed. The simulation performance of the scheme is presented in terms of the MSEE and the bit error rate (BER) of the OFDM system. Such a scheme is attractive in high data rate scenarios in closed loop OFDM systems.     

Joint I/Q imbalances estimation using data-dependent superimposed training

In this paper, we address the joint estimation of the channel impulse response and frequency-dependent in-phase and quadrature-phase (I/Q) imbalances using data-dependent superimposed training (DDST). The analysis developed shows that it is possible to use the first-order statistics of the received process to achieve synchronization and identify the resulting widely linear system that encompasses the radio frequency impairments considered. Furthermore, it is also verified that for the joint estimation of the transmitter and the receiver I/Q imbalances, additional constraints than those required for strictly linear systems should be imposed on the training sequences employed. The results of numerical simulations show that DDST has comparable performance with methods reported in the literature.     

跳频OFDM通信系统同步算法及实现

结合跳频通信技术抗干扰和正交频分复用(OFDM)技术节省频带资源的优点,提出了一种通信帧格式及其同步算法。该算法构造了每个载波上传输隐藏伪随机序列的OFDM数据,利用伪随机序列良好相关特性实现了定时估计和粗频偏捕获,再使用ML估计算法进行细频偏估计,能够使OFDM技术应用到跳频通信领域。仿真和实验结果表明,在多径信道下,提出的算法单个定时估计和频偏估计误差小。     

正交频分复用系统信道估计技术研究

在OFDM系统中,为了能达到系统需要的解码要求,必须进行准确的信道估计.首先介绍了OFDM系统基本原理,对基于叠加训练序列的OFDM系统信道估计算法进行了仿真分析,并比较了在不同ρ值情况下的性能,从而存在一个最优的ρ值取得最好的估计性能.