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 IBT 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 three-order Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.     

Basis expansion model for channel estimation in LTE-R communication system

This paper investigates fast time-varying channel estimation in LTE-R communication systems. The Basis Expansion Model (BEM) is adopted to fit the fast time-varying channel in a high-speed railway communication scenario. The channel impulse response is modeled as the sum of basis functions multiplied by different coefficients. The optimal coefficients are obtained by theoretical analysis. Simulation results show that a Generalized Complex-Exponential BEM (GCE-BEM) outperforms a Complex-Exponential BEM (CE-BEM) and a polynomial BEM in terms of Mean Squared Error (MSE). Besides, the MSE of the CE-BEM decreases gradually as the number of basis functions increases. The GCE-BEM has a satisfactory performance with the serious fading channel.     

基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计

为了进一步提升车联万物(V2X)的通信性能,首先根据信道冲激响应的稀疏性建立了适用于高速移动场景的基扩展模型(BEM);其次,证明了BEM系数具有稀疏性,将信道估计问题转化为稀疏信号重构问题,进而提出基于BEM的改进正则化正交匹配追踪(iROMP)迭代稀疏信道估计算法(简称为BEM-iROMP算法).所提算法通过iRO...     

OFDM联合信道估计与均衡技术研究

OFDM系统在时变信道中会受到子载波间干扰,单独进行信道估计和信号检测的策略对于提高接收机的抗干扰能力有限,将信道估计和信号检测统一考虑则可更有效地抵抗子载波间干扰。针对此问题,基于迭代SAGE算法提出了一种新的联合信道估计与符号检测算法,为了减低算法的复杂度,引入BEM信道建模方法。仿真评估了BEM算法的归一化均方误差性能,验证了BEM建模的有效性,显示该算法的误码率优于基于BEM算法的线性均衡检测算法和基于MMSE的干扰对消算法,而且该算法只通过少数几次迭代便可达到收敛域,较好地克服了子载波间干扰的问题。     

Doubly-selective fading channel equalization: A comparison of the Kalman filter approach with the basis expansion model-based equalizers

In this paper, we exploit the Kalman filter as a time-varying linear minimum mean-square error equalizer for doubly-selective fading channels. We use a basis expansion model (BEM) to approximate the doubly-selective channel impulse response. Several time-varying linear equalizers have been proposed in the literature where both the channel and the equalizer impulse responses are approximated by complex exponential (CE) BEMs. Our proposed Kalman filter formulation does not rely on a specific BEM for the underlying channel, therefore, it can be applied to any BEM, including the CE-BEM and the discrete prolate spheroidal (DPS) BEM. Moreover, the Kalman filter relies solely on the channel model and therefore, does not incur any approximation error inherent in the CE-BEM representation of the equalizer. Through computer simulations, we show that compared to two of the existing algorithms, the proposed Kalman filter formulation yields the same or an improved bit error rate at a much lower computational cost, where the latter is measured in terms of the number of flops needed for the equalizer design and implementation.     

Time-varying channel estimation in OFDM systems based on multiple symbols BEM

In view of the problem of low frequency spectrum utilization for the time-varying channel estimation in orthogonal frequency division multiplexing(OFDM) systems based on basis expansion model (BEM),a new method based on multiple symbols BEM was proposed.Compared with the conventional method based on single symbol,the proposed method reduced the subcarriers for pilots.And by means of the baseline tilting technique,the new method mitigated the Gibbs phenomenon caused by Fourier series expansion.The theoretical analysis reveals that the channel model error is reduced due to the introduction of the baseline tilting technique when the normalized Doppler frequency is no more than $\frac{\frac{1}{2}-\frac{1}{M}}{1+\frac{N_g}{N}}$.Finally,the numerical simulation shows that compared with conventional methods,the proposed algorithm achieves significant improvement on the estimation error performance and bit error rate (BER) performance under the conditions of the corresponding time-varying channel.     

MIMO-OFDM系统中一种改进的Bayesian EM信道估计器

该文提出了MIMO-OFDM系统中一种改进的Bayesian EM信道估计器。利用软球形译码器的搜索列表和解码器反馈的先验信息对传统EM信道估计中的软信息近似处理进行了修正,计算了更为准确的软符号后验概率分布以及一阶、二阶统计量。基于初始估计得到的信道先验信息,设计了新的考虑软符号后验互相关的时域信道冲激响应最大后验概率(MAP)估计算法。仿真试验结果表明:该算法和传统EM信道估计算法相比具有更低的误码率和更小的估计均方误差值。     

Variable forgetting factor linear least squares algorithm forfrequency selective fading channel estimation

In this article, the variable forgetting factor linear least squares algorithm is presented to improve the tracking capability of channel estimation. A linear channel model with respect to time change describes a time-varying channel more accurately than a conventional stationary channel model. To reduce the estimation error due to model mismatch, we incorporate the modified variable forgetting factor into the proposed algorithm. Compared to the existing algorithms-exponentially windowed recursive least squares algorithm with the optimal forgetting factor and linear least squares algorithm-the proposed method makes a remarkable improvement in a fast fading environment. The effects of channel parameters such as signal-to-noise ratio and fading rate are investigated by computer simulations     

基于总体最小二乘准则的OFDM系统时变信道估计

信道估计的准确程度直接影响正交频分复用系统的性能。为了提高时变信道估计算法的精度,基于总体最小二乘准则( TLS)提出了一种时变信道的估计方法。该方法用线性模型对时变信道进行建模,不仅考虑了信道噪声,同时也兼顾了模型误差。该方法能较好地跟踪信道的变化,显著消除模型误差。仿真结果表明所提算法的均方误差介于最小二乘算法与最小均方误差算法之间,在不同归一化多普勒频移下,该算法具有较好的稳健性。     

Block adaptive techniques for channel identification and datademodulation over band-limited channels

A new approach to the problem of data detection for communications over band-limited channels with unknown parameters is introduced. We propose a new way to implement the Viterbi algorithm (VA) for maximum-likelihood data sequence estimation (MLSE) in a known channel environment and utilize it to derive block adaptive techniques for joint channel and data estimation, when the channel-impulse response (CIR) is unknown. We show, via simulations, that we can achieve a probability of error very close to that of the known channel environment and nearly reach a mean-square error in the channel estimate as predicted by analytical bounds, operating on static channels, which exhibit deep nulls in their magnitude response and nonlinear phase. The proposed schemes accomplish channel acquisition after processing a few hundred symbols while operating without a training sequence, whereas linear blind equalizers, such as Sato's (1975) algorithm, fail to converge at all. The application of block processing to adaptive MLSE is also investigated for time-varying frequency-selective Rayleigh-fading channels, which are used for modeling mobile communication systems. In such environments it is shown that the proposed scheme exhibits improved performance compared to the conventional adaptive MLSE receiver using tentative delayed decisions     

基于扩展路径识别算法的水声OFDM系统低复杂度迭代稀疏信道估计

移动OFDM水声通信系统中,基于压缩感知的稀疏信道估计方法计算量较大,不适用于实时通信。针对这一问题,该文基于一致多普勒信道模型提出一种扩展路径识别(GPI)算法。该方法首先使用信道多普勒扩展矩阵构造等效发射序列,将多普勒信道转化为等效线性时不变信道。然后使用GPI算法估计信道多普勒及各路径的时延及幅度参数,实现低复杂度稀疏信道估计。此外,该文将GPI算法扩展到Turbo接收机中,通过利用信道译码器反馈的数据符号先验信息迭代提高信道估计精度。仿真结果表明,所提方法的性能优于传统的路径识别算法,且与OMP算法接近,而其计算量远低于后者。     

基于基扩展模型的UKF-RTSS高可靠鲁棒V2V信道估计

车联网应用场景对无线通信在带宽、时延、可靠性方面提出了更高的需求,特别是车辆对车辆(Vehicle to Vehicle, V2V)场景。针对V2V高速移动场景,时/频域选择性衰落(双选衰落)和非平稳特性给信道估计带来的技术挑战,该文提出了一种基于基扩展模型(Basis Expansion Model, BEM)的UKF-RTSS (Unscented Kalman Filter- Rauch-Tung-Striebel Smoother)信道估计方法。该方法采用BEM拟合快时变信道,将信道参数的估计转化为基函数系数的估计;通过无迹卡尔曼滤波(UKF),联合估计数据处信道冲激响应与时域自相关系数,用于追踪快时变的信道响应。为了进一步提升信道估计的精度,引入RTSS对后向信道状态信息进行信道估计和插值,与UKF构成了“滤波和平滑”结构的UKF-RTSS联合估计器。系统仿真分析表明,在不同速度的快时变条件下,所提方法相比其他经典方法具有更高的信道估计精度和鲁棒性,特别适用于车联网下的无线通信场景。     

Algorithm of Robust Frequency Estimation in a Channel with White Gaussian Noise and Pulse Interferences

An algorithm for robust frequency estimation in a channel with additive white Gaussian noise and pulse interference is proposed. The algorithm involves iterative elimination of pulse interferences and subsequent calculation of the maximum likelihood estimate. It is demonstrated that the proposed filter-cleaner differs from the well-known Martin–Thomson filter in the functional dependence of adaptive coefficients on the estimates of parameters of distributions of the linear prediction error and the amplitude envelope of the signal. The results of mathematical modeling and numerical estimation of the threshold point of the method (0.49) are presented.     

Frequency-selective multiple-input multiple-output channel estimation with transmitter non-linearities

The authors propose an algorithm based on the knowledge of training sequences to obtain an asymptotically unbiased estimator of non-linear multiple-input multiple-output (MIMO) channels, which involves the radio frequency front-end non-linearity and linear frequency selective MIMO channels. Although the impact of non-linearity in the transmitter side has been widely studied, most work on the channel estimation assumes linear channel models and ignores the non-linear effects. In this study, we develop a nonlinear channel estimator that can simultaneously estimate the linear MIMO channel model and non-linearity of the transmitter is developed. With these two sets of parameters, the non-linear channel model can be fully described. This channel estimation algorithm is implemented over an empirical MIMO channel model using an orthogonal frequency division multiplexing system.     

无线OFDM系统传输信道模型化参数检测

利用导频子载波时频相位相关性统计检测与QAM信号解映射判决误差统计，提出了一种适用于频域导频无线OFDM系统的传输信道模型化参数检测方案，包括信道最大多径时延、最大多普勒频率和信噪比检测；能够有效解决传统的OFDM信道估计算法通常按照最恶劣信道情况上限进行设计，以及基于MMSE准则最优信道估计器中信道先验信息统计运算复杂度高的缺陷；仿真结果表明，在低复杂度条件下可以有效获知当前传输信道模型参数的近似统计信息。     

Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems

Massive MIMO (multiple-input-multiple-output) is one of the key technologies of 5G mobile cellular networks, which can form a huge antenna array by providing a large number of antennas at the cell base station. It will greatly improve the channel capacity and spectrum utilization and has become a hotspot in the field of wireless communications in recent years. Aiming at the high complexity of channel estimation algorithm for massive MIMO system, a sparse channel estimation algorithm with low complexity is proposed based on the inherent sparsity of wireless communication channel. The algorithm separates the channel taps from the noise space on the basis of the traditional discrete Fourier transform (DFT) channel estimation, so that the channel estimation only needs to calculate the part of the channel tap, so the computational complexity of the algorithm is greatly reduced. The simulation results show that the proposed algorithm can achieve near minimum mean square error (MMSE) performance while maintaining low complexity. Moreover, the Bit Error Rate and Inter-Cell Interference also indicates that the proposed improved algorithm shows better overall performance than the conventional algorithms which makes it suitable from practical perspective.     

利用ICI自消除改善OFDM系统时变信道估计与均衡的性能

正交频分复用(OFDM)系统受到时变信道的影响,产生载波间干扰(ICI),导致系统性能严重下降。消除ICI最有效的方法是频域均衡,但这需要准确的信道估计。基扩展模型(BEM)能够准确逼近时变信道,从而将对信道的估计转化为对少量模型参数的估计。然而,在使用导频符号对参数进行估计时,来自相邻非导频符号的ICI干扰,却严重影响了估计的准确性。为此,通过分析子载波所产生的ICI系数的变化特性,该文提出采用ICI自消除方法来减小参数估计中所存在的ICI项,以提高估计的准确性,从而改善时变信道估计与均衡的性能。仿真结果验证了该方法的有效性,相比单纯基于BEM的信道估计方法,能够得到更准确的时变信道,以及更好的ICI消除效果。     

Time-domain channel estimator based on cyclic correlation for OFDM systems with guard interval

Channel impulse response （CIR） can be estimated on the basis of cyclic correlation in time-domain for orthogonal frequency division multiplexing （OFDM） systems, This article proposes a generalized channel estimation method to reduce the estimation error by taking the average of different CIRs. Channel impulse responses are derived according to the different starting points of cyclic correlation. In addition, an effective CIR length estimation algorithm is also presented. The whole proposed methods are more effective to OFDM systems, especialiy to those with longer cyclic prefix. The analysis and the simulation results verify that the mean square error performance is 4-5 dB better than the conventional schemes under the same conditions.     

Joint channel and phase noise estimation in OFDM systems at very high speeds

In this paper, the problem of joint phase noise and channel estimation for OFDM systems over a fast time-varying frequency-selective channel is explored. Each channel tap time-variation within one OFDM symbol is approximated by a Basis Expansion Model (BEM). Joint estimation is performed on multiple OFDM symbols via the Extended Kalman Filtering in order to exploit the time-correlation of the parameters. The data symbols are estimated by means of an iterative pilot-based algorithm. It is shown that, with only 2 iterations, our algorithm outperforms the conventional one, and the performance approaches that of the ideal case for which the channel response and phase noise are known.     

Structured multiuser channel estimation for block-synchronousDS/CDMA

Uplink channel estimation for a block-synchronous chip-asynchronous DS/CDMA system as proposed for the time-division duplex option of third-generation cellular systems is considered. Training midambles are employed for joint channel estimation of all users. The standard unstructured approach based on modeling the effective user channels as unknown FIR filters is compared with two structured methods that exploit a priori knowledge about the user channels such as the maximum delay-spread, the transmit chip-shaping pulse and the path delays. Since these are usually unknown, a low-complexity estimator for the path delays of all users is derived from a maximum-likelihood approach. For all channel estimators, optimal sets of training sequences based on perfect root-of-unity sequences are found. For these optimal sets, it is shown that the reduction in channel estimation mean-squared error of the structured estimator versus the unstructured estimator is exactly the ratio of the number of structured parameters to unstructured parameters. Simulation results show that structured channel estimation provide advantages up to 4 dB in terms of output signal-to-interference plus noise ratio with respect to unstructured estimation, for linear RINSE detection. In contrast, for conventional single-user matched filtering, unstructured estimation proves to be sufficiently good