A modified time domain interpolation method for LS channel estimation in OFDM systems

A modified time domain interpolation method is proposed for orthogonal frequency division multiplexing(OFDM)systems to address the problem that time domain interpolation in the least square(LS)channel estimation method based on comb-type pilots cannot choose the pilot spacing flexibly.Firstly,the estimated channel frequency response(CFR)at pilot positions in the frequency domain is obtained by LS channel estimation based on comb-type pilots,and the estimated channel impulse response(CIR)in the time domain is obtained by linear interpolation and inverse fast Fourier transform(IFFT).Secondly,the error of the estimated CIR obtained by linear interpolation is analyzed by theoretical deduction,and a method for correcting it is proposed.Finally,an estimated CFR at all subcarrier positions in the frequency domain is obtained by performing zero padding in the time domain and fast Fourier transform(FFT)on the modified CIR.The simulation results suggest that the proposed method gives similar performance to time domain interpolation,yet it does not need to meet the condition of time domain interpolation that the number of subcarriers must be an integral multiple of pilot spacing to use it.The proposed method allows for flexible pilot spacing,reducing the number of pilots and the consumption of subcarriers used for channel estimation.

New subspace algorithm for blind channel estimation in OFDM systems

In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.

Analysis and Comparison of Time Replica and Time Linear Interpolation for Pilot Aided Channel Estimation in OFDM Systems

This paper analyzes and compares two time interpolators, i.e., time replica and time linear interpolator, for pilot aided channel estimation in orthogonal frequency division multiplexing (OFDM) systems. The mean square error (MSE) of two interpolators is theoretically derived for the general case. The equally spaced pilot arrangement is proposed as a special platform for these two time interpolators. Based on this proposed platform, the MSE of two time interpolators at the virtual pilot tones is derived analytically;moreover, the MSE of per channel estimator at the entire OFDM symbol based on per time interpolator is also derived. The effectiveness of the theoretical analysis is demonstrated by numerical simulation in both the time-invariant frequency-selective channel and the time varying frequency-selective channel.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Enhanced EM-based channel estimation for MIMO-OFDM in highly mobile channels

An enhanced expectation maximization （ with channel time variation is proposed for mobile EM） based iterative channel estimator for coping multiple input multi output orthogonal frequency division multiplexing （MIMO OFDM） systems. In the proposed scheme, the recursive least squares （RLS） algorithm is applied to track the time varying channel impulse response （CIR） within several symbols. By using the tracked time varying CIR, the ICI are constructed and then cancelled from the received signal, thus reducing their impactions on the channel estimation. Moreover, based on an o ver sampled complex exponential basis expansion model （ OCE BEM）, an improved channel predic tor is derived in order to improve the initial channel estimates accuracy of the iterative estimator. Simulation results show that ying scenarios with a smaller the proposed scheme outperforms the classic counterpart in time var cost of complexity.

Channel Estimation with an Interpolation Trained Deep Neural Network

This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low estimation accuracy when estimating channel state information (CSI) with fewer pilots. To tackle the problem, in this paper, a deep learning model is first trained by the interpolated channel frequency responses (CFRs) and then used to denoise the CFR estimated by least square (LS) estimation. The proposed deep neural network (DNN) can also be trained in a short time because it only learns the CFR and the network structure is simple. According to the simulation results, the performance of the DNN estimator can be compared with the minimum mean-square error (MMSE) estimator. Furthermore, the DNN approach is more robust than conventional methods when fewer pilots are used. In summary, deep learning is a promising tool for channel estimation in wireless communications.

A simple channel estimator for space-time coded OFDM systems in rapid fading channels

A simple channel estimator for space-time coded orthogonal frequency division multiplexing (OFDM) systems in rapid fading channels is proposed. The channels at the training bauds are estimated using the EM (expectation-maximization) algorithm, while the channels at the data bauds are estimated based on the method for modelling the time-varying channel as the linear combination of several time-invariant " Doppler channels". Computer simulations showed that this estimator outperforms the decision-directed tracking in rapid fading channels and that the performance of this method can be improved by iteration.

Pilot Allocation Optimization Using Enhanced Salp Swarm Algorithm for Sparse Channel Estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally impractical.The meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this issue.Like most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow convergence.In this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local search.Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms.Besides,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.

Performance Evaluation and Analysis of Switching Algorithms in MIMO-OFDM System with Ideal and Non-Ideal CSI

In this paper we analyzed the bit error rate performance of a switching algorithm between spatial multiplex-ing and diversity for an OFDM MIMO system with ideal channel state information. The effect of channel estimation error was studied and we verified by simulations that the spatial multiplexing outperforms the switching algorithm. Given that the switching algorithm is based on the comparison of the channel matrix Demmel condition number to a threshold, its accuracy is compromised when channel estimation error in-creases. As a first intuitive solution, we proceeded to the adaptation of the threshold, but this didn’t lead to a pertinent improvement for the main reason that channel estimation errors did affect the MIMO techniques which use different constellation. Based on that, we proposed a new estimation technique that improved the bit error rate performance significantly.

High mobility channel estimation method based on improved basis expansion model

In this paper, the problem of high mobility channel estimation in the Long-Term Evolution for Railway (LTE-R) communication system is investigated. By using a Basis Expansion Model (BEM), the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients. By estimating the basis function coefficients, the fast time-varying channel can be approximated. In order to reduce the estimation error resulting from the high frequency basis function, the Generalized Complex Exponential BEM (GCE-BEM) is modified to form an Improved GCE-BEM (IGCE-BEM) by adding a correction coefficient to the basis function. Moreover, an Improved Baseline Tilting (IBT) method is proposed to reduce the Gibbs effect. In addition, linear interpolation, Gauss interpolation, and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions. The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error (NMSE). The IB T method is better than the BT method in reducing the Gibbs effect. In addition, combined with the IBT, the IGCE-BEM also has low NMSE under high moving speed and high noise power. The performance of the threeorder Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.

基于DFT-SWOMP的OFDM系统信道估计方法

基于压缩感知的OFDM信道估计方案中,分段弱正交匹配追踪(SWOMP)算法具有不需要预知信道稀疏度的优点,但其信道估计精度受输入的门限参数和迭代次数的影响较大。针对这一问题,提出一种基于DFT-LS算法的门限自适应的SWOMP算法改进方案。考虑到在OFDM系统中,保护间隔长度外的信道时域响应都可以视为噪声,因此该方案的核心思想是利用DFT-LS算法预估出噪声水平,并用此预估值来动态设置SWOMP算法的门限参数。同时,该方案还使用DFT-LS算法预估出的信道频域响应作为SWOMP算法的迭代停止条件。仿真结果表明,这种SWOMP算法的改进方案可以有效地估计出信道参数,并且相比SWOMP算法,其估计结果的MSE值在不同信噪比下都有不同程度的提升。

基于矩阵恢复的OFDM信道估计方法

正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)中至关重要的一项技术是信道估计,本文提出一种基于矩阵恢复的OFDM信道估计方法,将连续多个OFDM信号的频域信道构造成一个信道矩阵,由于这个信道矩阵是低秩的,所以可以将信道估计问题转换为信道矩阵的加权截断核范数最小化问题,并使用改进的奇异值阈值(Singular Value Thresholding,SVT)算法对信道矩阵进行恢复。仿真结果表明,本文提出的方法和传统信道估计算法相比,使用相同导频数可以获得更高的估计精度,在获得相同估计精度时,消耗导频数更少。与基于压缩感知的信道估计方法相比,本文方法消耗相同数量的导频,但可直接获得高精度的OFDM信道的频域估计。

一种基于DFT的低复杂度虚子载波OFDM信道估计算法

该文针对采用虚子载波的OFDM通信系统,提出了一种基于DFT的低复杂度信道估计算法,并与最小二乘(Least Square,LS)估计算法和线性最小均方误差(Linear Minimum Mean Square Error,LMMSE)算法进行了详细的性能和复杂度比较。所提DFT算法较好地降低了高斯白噪声的影响,相对于LS算法获得了较大的性能增益。该算法在复杂度与性能之间取得了较好的折衷,具有很好的实用价值。

OFDM系统中基于补零DFT信道插值算法的研究

针对OFDM系统中基于补零DFT实现信道插值的算法,对传统的时域方法和变换域方法进行了分析,并提出了一种新的补零方法。首先通过理论分析指出传统的基于补零DFT实现信道插值算法中,时域方法和变换域方法是等效的;通过对信道插值算法中冲激响应估计序列进行补零的分析,指出补零操作是为了重构出冲激响应的N点采样序列(N表示总子载波数),并针对传统补零方法在一定条件下产生的重构误差,提出了一种新的补零方法。仿真结果表明以上理论分析是正确的,新补零方法可以减小一定条件下的重构误差,增强信道插值算法对信道延迟分布的鲁棒性,有效提高系统的性能。

基于LS算法的OFDM信道估计的研究与改进

OFDM信道估计中,LS算法因其运算简单,得到了广泛应用。但是在实际的系统中,由于有非理想因素的存在,使得该算法受到载波间干扰(ICI)噪声的影响;并且突发传输时,信道响应会受到外界噪声的干扰,使估计的信道响应幅值在一定范围内上下波动,并带有尖峰和毛刺。为了解决此问题,提出了一种基于LS算法的最优化FIR滤波器(切比雪夫滤波器)信道估计算法。切比雪夫滤波器在过渡带衰减很快,和理想滤波器的频率响应曲线之间的误差最小,因此该算法较之传统的加窗算法,能保证局部频率点的性能也是最优的,进而有效地减小均方误差(MSE)。在高斯白信道环境下引入突发噪声,对所提方案进行了仿真,其结果验证了该方法能有效消除通带内因突发传输引起的毛峰和尖刺。

MIMO-OFDM系统中的信道估计算法及仿真

对MIMO-OFDM系统的多种信道估计算法进行了探讨,重点对基于导频的信道估计算法LS、MMSE以及ZP-OFDM的盲信道估计算法进行了分析和比较,并对LS、MMSE以及ZP-OFDM的盲信道估计算法分别进行了仿真实验,仿真结果表明了算法的有效性,对盲信道估计算法的研究有很好的参考价值.

频率选择性衰落OFDM的半盲信道估计算法研究

从系统性能分析和设计的角度详细地研究了基于无线HIPERLAN2通信协议的OFDM(orthogonal frequency division multiplexing,正交频分多路复用技术)系统信道估计与均衡的一种半盲算法,提出了结合直接法和Cholesky分解法的切换盲算法。这种半言算法综合了全盲算法得到的信息与已知导频符号,充分利用了原发信号的统计特性和OFDM帧结构中插入的导频符号,克服了全盲和导频训练序列存在的问题,且不需额外的带宽。仿真结果表明,在误比特率和收敛性方面,该算法比现有的主要三种全盲算法有更好的收敛和抗干扰特性。

OFDM系统信道估计中插值算法性能分析

对基于导频的OFDM系统中信道估计的各种插值算法的性能进行了对比分析,对基于离散傅里叶变换的插值算法和其他插值算法进行了分析.计算机仿真证明了基于FFT的插值算法的插值精度在高信噪比条件下高于其它算法.

MIMO-OFDM系统中基于RAMP及其改进的稀疏信道估计算法

研究了在MIMO-OFDM系统中的稀疏信道估计问题.将正则化稀疏度自适应匹配追踪算法(RAMP)运用到MIMO-OFDM稀疏信道估计中,并对该算法的迭代结束条件加以改进,取残差的能量之差小于设定的阈值来终止迭代过程,更加准确地估计出信道稀疏度,进而提高了稀疏信道的估计精度.仿真结果表明,在MIMO-OFDM系统中,相比RAMP算法与稀疏度自适应匹配追踪算法(SAMP),改进算法能够获得更好的MSE性能,在不需要稀疏度的前提下达到了与正交匹配追踪算法(OMP)算法相似的MSE性能.

OFDM系统中基于导频的时变信道估计

该文对OFDM系统中基于导频的时变信道估计进行了研究,采用分组导频对一个OFDM符号间隔内某些时刻的信道冲击响应值进行估计,提出一种利用低通插值估计一个符号间隔内其他时刻的信道值的方法.仿真结果表明该文采用的低通插值方法所得到的估计均方误差要小于用线性插值或直接用信道模型产生的系数进行插值所得到的估计均方误差.