一种低复杂度的DFT-S-OFDM系统迭代检测算法

针对LTE上行的单载波频分多址系统--离散傅立叶变换扩频的正交频分复用(DFT-S-OFDM)系统,提出了一种低复杂度的迭代检测实现方法.由于传统方法根据发射机与信道级联系统的等效传输矩阵,完成最小均方误差(MMSE)的迭代检测,但非对角矩阵求逆复杂度较高,因此,所提出的新方法对发送端DFT扩频后的信号先进行单点的MMSE检测,然后根据逆离散傅立叶变换(IDFT)解扩后的后验均值和方差等效得到输出外信息比特似然比.仿真结果表明,所提出迭代检测接收算法的性能与传统方法相近,而其实现复杂度则有较大降低.     

无线城域网OFDM系统信道估计算法

OFDM技术是无线城域网IEEE802.16a标准中物理层面的主流调制技术,今建立符合IEEE802.16a标准的OFDM信道估计模型,阐述其导频基本信道估计方法的原理及特点,分析比较了LS/MMSE/LMMSE/SVD四种算法的MSE和BER性能特点.仿真结果说明,LMMSE算法的均方误差和误码率性能最好,且计算复杂度相对较低,易于物理实现.     

OFDM水声通信线性最小均方误差算法信道均衡

正交频分复用在高速水声通信中的应用越来越广泛.水声信道是多径、高噪声的衰落信道.由于信道条件差,容易产生码间干扰,信道均衡技术是提高通信系统性能的重要技术.提出了线性最小均方估计在水声通信系统中应用的方案,研究了实际通信中利用调频信号如何确定信号的平均多径时延、最大多径时延,比较了不同调频信号测量信道的性能.该方案基于导频辅助均衡方法,利用调频信号测量信道,获得的信道统计信息,计算信道的自相关矩阵.从而根据线性最小均方误差算法进行信道均衡.通过仿真和湖试进行验证,结果表明该方案效果较好,对系统性能改善比较明显.     

A joint iterative channel estimation and symbol detection algorithm for OFDM systems over time-varying channels

针对正交频分复用(OFDM)系统中由于信道快时变导致的子载波间干扰(ICI),基于基扩展模型,提出了一种时变信道下OFDM系统联合迭代信道估计与符号检测算法.该算法将基于基扩展模型的最小均方误差信道估计算法与一种类似于串行干扰抵消的符号检测方式相结合,并利用插值和滤波过程进一步消除噪声影响,进而通过联合迭代获得较为精确的符号检测结果.同时,利用带状矩阵近似降低算法的计算复杂度,在复杂度与算法性能之间取得比较好的折中.理论分析和仿真结果表明,在快时变信道条件下,这种新的联合迭代信道估计与符号检测算法可有效地提高系统性能.     

基于导频时分复用的多中继协同通信系统频域信道估计算法

针对频率选择性衰落环境下结合正交频分复用技术的放大转发多中继协同通信系统,提出了基于导频时分复用的频域信道估计算法,包括最小二乘(LS)估计算法和低阶近似的线性最小均方误差(Lr-LMMSE)估计算法.理论分析和仿真结果表明,该算法不仅能成功分辨多中继协同通信系统的所有频域信道衰落系数,避免各中继节点转发的导频符号在目...     

基于差分进化的时变信道最大似然估计算法

研究了隧道环境下的通信信道估计。针对隧道环境的地铁列车与轨旁设备之间无线通信中无线传输信道快速变化的特点,提出了一种采用元胞差分进化(DE)方法实时获取时变信道的有效信道长度的新型最大似然(ML)信道估计算法——DE-ML算法。仿真结果表明该算法在使用较少导频信息的情况下,通过差分进化方法有效估计跟踪有效信道长度,其估计性能优于最小二乘(LS)、线性最小均方误差(LMMSE)、传统ML等经典信道估计算法。该算法能在提高系统传输效率的同时显著提高算法的估计精度,尤其在高速移动情况下也具有了非常良好的性能。     

强多径干扰下的水声通信均衡算法研究

水声信道的典型特点为强多径干扰、多普勒频移严重。锁相环-判决反馈均衡器(Phase-Lock Loop-Decision Feedback Equalization,PLL-DFE)是水声相干通信中克服信道多径干扰,消除码间干扰的主要技术手段。对抗多径干扰的判决反馈均衡自适应算法进行了改进,在快速自优化LMS算法的基础上进行了优化,提出了记忆快速自优化均衡(Memory Fast-Optimized LMS,MFOLMS)算法。该算法提高了均衡器的跟踪性能。在二阶锁相环和判决反馈均衡器的联合作用下,按照最小均方误差(Minimum Mean Square Error,MMSE)准则自动调节相应的参数,使均衡器达到最佳的性能。仿真和湖试数据处理结果均表明,该文提出的算法可以抵消多径的影响,提高通信系统的接收性能。     

基于MMSE滤波的虚子载波梳状导频OFDM系统信道估计

分析了带有虚拟子载波并采用梳状导频的正交频分复用通信系统的信道估计过程中,在使用基于离散傅立叶变换的内插的情况下由于频域导频分布不均匀导致的时域信道响应泄漏的表现形式,针对泄漏造成的信道估计结果不精确的问题,提出了一种基于时域最小均方误差(MMSE)滤波的信道估计补偿方法.该方法通过滤波矩阵的作用补偿了时域信道估计结果...     

OFDM系统在时变信道下的迭代干扰抵消算法

为克服正交频分复用(OFDM)系统中因时变多径衰落信道破坏子载波的正交性引起载波间干扰(ICI),从而导致系统性能下降的问题,提出了一种基于最小均方误差(MMSE)估计和迭代算法相结合的迭代干扰抵消算法.为了消除一般MMSE均衡方法将载波间干扰当作加性高斯白噪声(AWGN)所带来的误差,该算法先使用MMSE方法对传输信号进行初值估计,再通过迭代干扰抵消和MMSE均衡相结合,来消除由时变信道所带来的载波间干扰.多次仿真表明,在时变多径衰落信道中使用本算法能够有效地消除载波间干扰,使系统性能得到很好的改善.     

一种改进的多通道盲信号解相关算法

针对多通道解相关算法在混合信号协方差矩阵的最大特征值较大或者最大与最小特征值的比值很大(大于106的病态混合)时收敛速度变慢且收敛误差增大的不足,本文引入归一化方法,即在迭代过程中对解混信号进行归一化,从而限制其协方差矩阵最大特征值的取值范围,且降低最大与最小特征值的比值.数值仿真表明,改进后的算法在降低迭代误差和加强...     

Decision-directed channel estimation based on iterative linear minimum mean square error for orthogonal frequency division multiplexing systems

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.     

High mobility orthogonal frequency division multiple access channel estimation using basis expansion model

Owing to the loss of subcarrier orthogonalities in high-speed applications, the use of conventional frequency-domain-based channel estimation in high mobility orthogonal frequency division multiple access (OFDMA) systems such as mobile WiMax may give rise to an unacceptable high channel estimation error floor. To alleviate this problem, the authors develop some basis expansion model (BEM)-based estimation schemes for the OFDMA uplink. Specifically, the authors express the time-varying channel as a superposition of a small number of complex exponential basis functions spanning the entire Doppler range, and then formulate least square (LS) and linear minimum mean square error (LMMSE) algorithms to estimate the basis coefficients for two different types of pilot patterns. The authors also derive the respective Cramer-Rao lower bounds for these estimators. It has been shown that the time domain BEM using a pilot scheme where pilots are placed over time axis will give better performance under a high Doppler scenario. Lastly, using simulation results, the proposed algorithms have been found to have better estimation accuracy over current frequency domain estimation techniques. This is in addition to the advantage that the proposed algorithms have in general a lower computational complexity.     

Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems

In this paper, a novel precoding scheme based on the Gauss-Seidel (GS) method is proposed for downlink massive multiple-input multiple-output (MIMO) systems. The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process. In addition, the GS method shows a fast convergence rate to the Zero-forcing (ZF) method that requires an exact invertible matrix. However, to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels, more iterations are necessary for the GS method and increase the overall complexity. For efficient approximation with fewer iterations, this paper proposes a weighted GS (WGS) method to improve the approximation accuracy of the GS method. The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square (LS) method. After the computation of weights, the different weights are applied for each iteration of the GS method. In addition, an efficient method of weight computation is proposed to reduce the complexity of the LS method. The simulation results show that bit error rate (BER) performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.     

Channel estimation for time-hopping pulse position modulation ultra-wideband communication systems

Ultra-wideband (UWB) communication systems are used in indoor environments with dense multi-path characteristics. Therefore channel estimation has an important role in the receiver of these systems. A new approach for data-aided (DA) and non-data-aided (NDA) channel estimation is proposed, which is called the pulse compression (PC) method. This method is useful for UWB systems employing time-hopping pulse position modulation. The PC method requires only some basic operations such as sampling, overlap-add and finite impulse response filtering. The PC method, in both DA and NDA scenarios, in spite of its low complexity, outperforms the maximum-likelihood (ML) method in channel parameters estimation. The bit error rate (BER) of the DA method, in single-user scenario, performs as well as the ML method, and in multi-user scenario, in the worst case, there is only 0.5 dB loss compared with the ML method. In the case of NDA scenario, the proposed method outperforms the NDA-ML method, that is, in the single-user scenario about 4 dB gain at the BER of 1023 is observed. In multi-user scenario, it outperforms significantly the NDA-ML method, and its performance loss in comparison with the perfect channel knowledge scenario is about 3 dB at the BER of 1023.     

Efficient MIMO channel estimation using complementary sequences

Low complexity channel estimation for single-carrier block transmission systems over multiple-input multiple-output time varying frequency-selective channels is investigated. A time slot structure that uses Golay complementary sequences with perfect periodic autocorrelations as two-sided pilot blocks is presented. Employing this structure, optimal least square estimate in the minimum mean square error (MMSE) sense is achieved. Furthermore, a computationally efficient algorithm which is named as fast periodic Golay correlation is proposed based on the specific generator and the properties related to circulant matrices. Finally, the simulation results show the MMSE performance of the proposed scheme and algorithm.     

Reducing the peak-to-average power ratio using unitary matrix transformation

A theoretical analysis is presented to show that in orthogonal frequency division multiplexing systems, the peak-to-average power ratio (PAPR) can be reduced by performing a unitary matrix transformation on the input vector of N symbols. The authors also prove that this transformation does not degrade the bit error rate (BER) or power spectral density (PSD) performance. Based on this, the inverse discrete Fourier transform matrix is proposed as the unitary matrix to reduce the PAPR. The simulation results show that the proposed scheme can obtain significant PAPR reduction while maintaining good performance in the BER and the PSD. To further evaluate the performance of the proposed scheme, the authors compare it with some well known PAPR reduction techniques by simulations. It is demonstrated that the proposed scheme can offer better system performance and achieve a better compromise with regard to the PAPR reduction, BER, spectral efficiency and computational complexity.     

基于深度神经网络的水声FBMC通信信号检测方法

针对传统水声滤波器组多载波（Filter Bank Multi-Carrier,FBMC）通信接收端需经过信道估计和均衡才可恢复出发送符号,系统复杂度高且信道估计精度不佳等问题。文章将深度神经网络融入到水声多载波通信当中,提出一种基于深度神经网络的水声FBMC信号检测方法。在训练阶段通过大量的数据迭代、调试超参数和优化算法来改善深度神经网络参数,使其具有预期的估计效果。利用训练完成的深度神经网络模型取代传统FBMC通信系统接收端的信道估计、均衡等模块,自适应地学习水声信道状态信息,同时避免了固有的虚部干扰影响。在测试阶段直接将频域序列作为网络的输入来预测发送的二进制序列,仿真结果表明所提出的基于深度神经网络的FBMC信号检测方法相比传统信道估计算法有更好的误码率性能。     

OFDM水声通信系统动态OMP信道跟踪算法

针对正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)水声通信系统中最小二乘(Least Square,LS)信道估计算法和静态压缩感知信道估计算法分别存在估计精度低、导频开销大和计算复杂度高、实时性差的缺点,利用水声信道冲激响应的时域相关性,通过建立动态稀疏观测模型,提出一种动态正交匹配追踪(Dynamic Orthogonal MatchingPursuit,D-OMP)信道跟踪算法。该算法仅在初始时刻进行一次完整的正交匹配追踪(OrthogonalMatching Pursuit, OMP)信道估计获取信道支撑集,之后通过连续跟踪前一时刻信道支撑集的变化来跟踪信道。仿真结果表明,在导频开销相同的情况下,与传统LS算法、经典OMP算法相比,所提算法具有更好的信道跟踪性能和较低的算法复杂度。