一种MC-CDMA系统LS信道估计的改进算法

LS信道估计算法计算量小,实现简单,但是受噪声的影响信道估计精度低。为降低噪声对信道估计精度的影响,提出一种基于DFT的LS信道估计改进算法。该算法利用MC-CDMA中闲置的虚载波对信道噪声功率进行估计,通过将LS信道估计的时域响应限制在循环前缀长度内并进一步通过设置门限值忽略LS信道时域响应在循环前缀长度内的噪声分量和无效径响应,显著提高了信道估计精度。本文的算法运算量小,实现简单,信道估计精度高,仿真结果验证了算法的有效性。     

基于DFT的OFDM系统信道估计改进算法

基于DFT的信道估计算法计算复杂度比MMSE算法低,性能比LS算法好.但是传统的基于DFT的信道估计只消除了信道冲击响应估计中循环前缀长度之外的噪声,循环前缀长度内的噪声并没有得到抑制,因此算法性能还有提高的空间.本文提出了一种改进的基于DFT的信道估计算法.算法首先估计出噪声方差,然后利用噪声方差设定一个门限,通过此门限对循环前缀内的信道时域冲击响应值进行阈值,进一步消除噪声的干扰.仿真证明,本文的改进信道估计算法性能优于原算法.     

一种MC-CDMA最小二乘信道估计的改进算法

文中提出一种LS信道估计改进算法,该算法利用MC-CDMA中闲置的虚载波对信道噪声功率进行估计,通过将LS信道估计的时域响应限制在循环前缀长度内,并进一步利用信道噪声功率估计值,设置门限值忽略LS信道时域响应在循环前缀长度内的噪声分量和无效径响应。文中的算法运算量小,信道估计精度高,仿真结果验证了算法的有效性。     

基于迭代的FH-OFDM小数倍频偏估计算法

为了提高跳频正交频分复用（FH—OFDM）系统载波同步估计精度,提出了一种使用信道估计结果辅助,3次迭代的载波和定时估计算法。通过对接收信道噪声的估计确定门限逻辑,实现了高精度小数倍频偏估计。仿真结果表明,20dB信噪比时,在高斯白噪声下,新算法的估计精度高于基于循环前缀的算法10倍;在多径信道下,新算法的估计精度高于基于循环前缀的算法100倍;实验表明,使用该算法接收机性能优于基于循环前缀的算法。实验结果表明,该算法星座收敛较好。     

一种改进的基于DFT的OFDM信道估计算法

传统的DFT信道估计算法计算复杂度比MMSE算法低,性能比LS算法好。但是它只消除了信道冲击响应中循环前缀长度之外的噪声,而循环前缀长度以内的噪声并没有进行任何抑制,从而影响了算法的性能,说明性能存在提高的空间。对传统的DFT信道估计算法略加改进,仅仅在循环前缀长度内引入阈值门限,通过阈值门限进行阈值处理,就可以进一步消除噪声的影响。仿真表明：改进后的算法性能明显优于原算法,具有很好的实用价值。     

OFDM系统中基于SNR估计的改进DFT信道估计算法

为提高目前无线通信系统中正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术下的信道估计性能,分析了基于离散傅里叶变换(Discrete Fourier Transform,DFT)的信道估计算法存在的缺陷.针对传统DFT信道估计算法没有考虑循环前缀内噪声的...     

基于DFT的短波OFDM系统信道估计改进算法

传统的DFT算法利用信道估计冲击响应的长度小于循环前缀长度的原理,滤除了循环前缀码长度以外的噪声,循环前缀内的估计值仍存在噪声,结合传统DFT信道估计算法,提出了一种短波OFDM系统的基于DFT的改进信道估计算法,此算法首先进行传统的DFT信道估计算法,滤除部分噪声信号。再根据信道脉冲响应的幅值选取幅值最大的Ng/2个信号作为有用信号,然后用DFT得到的估计值的平均值替换其余Ng/2个信号,最后得到基于DFT改进算法的信道估计信号。仿真结果表明该算法性能有很大提高。     

无CP的OFDM联合信道估计和干扰抵消

无循环前缀OFDM无线通信系统可以提高频谱利用效率。提出一种基于无循环前缀(CP)OFDM系统的联合信道估计和干扰抵消算法。采用最小二乘法(Least Square)估计信道时域矢量,通过离散傅里叶变换(DFT)得到相应的OFDM信道的频域特性,由迭代算法对信号进行联合检测和干扰抵消。仿真结果表明,信道估计结果能够达到较高的精度,误码率也接近有循环前缀的OFDM无线通信系统。     

混合载波调制系统中的阈值DFT信道估计算法

传统的DFT信道估计算法复杂度比最小均方误差算法低,并且其性能比最小二乘算法好。但它只消除了循环前缀以外的噪声,对循环前缀以内的噪声并没有进行处理。鉴于该算法的不足,提出了一种混合载波调制下新的阈值DFT信道估计技术,即在循环前缀内设置了阈值,将大于阈值的数据保留,小于阈值的数据置为零,从而抑制循环前缀以内的噪声。仿真结果表明,与传统的DFT信道估计方法相比,提出的算法使循环前缀以内的噪声得到有效抑制,且在不同的调制参数下性能均得到优化。     

基于压缩感知的OFDM系统信道估计方法

针对无线信道的时域稀疏性以及稀疏度未知的问题,文章将压缩感知技术应用到正交频分复用(OFDM)系统信道估计中,提出了一种稀疏度自适应正交匹配追踪信道估计算法。算法利用离散傅里叶变换(DFT)信道估计算法对循环前缀内和外的噪声进行处理,估计得到的信道频率响应作为正交匹配追踪(OMP)算法稀疏迭代终止的判断条件,实现稀疏度自适应信号重建。同时在原子预选阶段,采用Dice系数准则代替内积准则作为相关性度量准则,可达到更优的估计性能。仿真结果表明,该算法相比于传统的压缩感知信道估计算法具有较好的性能,可以提高系统的归一化均方误差(NMSE)和误码率(BER)性能。     

Parametric channel modeling based OFDM channel estimation

A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estimation of channel parameters and channel interpolation. The exponentially embedded family （EEF） criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors＇ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique （ESPRIT）. Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error （MSE）. It is demonstrated that this approach exhibits a superior performance over the existing algorithms.     

DWDM传输系统信道串扰与信道间隔及信道带宽的关系

根据ITU－T建议的DWDM最小信道间隔标准,定量分析了DWDM传输系统的信道串扰与信道间隔及与信道带宽的关系。     

Blind channel approximation: effective channel order determination

A common assumption of blind channel identification methods is that the order of the true channel is known. This information is not available in practice, and we are obliged to estimate the channel order by applying a rank detection procedure to an “overmodeled” data covariance matrix. Information theoretic criteria have been widely suggested approaches for this task. We check the quality of their estimates in the context of order estimation of measured microwave radio channels and confirm that they are very sensitive to variations in the SNR and the number of data samples. This fact has prohibited their successful application for channel order estimation and hits created some confusion concerning the classification into under- and over-modeled cases. Recently, it has been shown that blind channel approximation methods should attempt to model only the significant part of the channel composed of the “large” impulse response terms because efforts toward modeling “small” leading and/or trailing terms lead to effective overmodeling, which is generically ill-conditioned and, thus, should be avoided. This can be achieved by applying blind identification methods with model order equal to the order of the significant part of the true channel called the effective channel order. Toward developing an efficient approach for the detection of the effective channel order, we use numerical analysis arguments. The derived criterion provides a “maximally stable” decomposition of the range space of an “overmodeled” data covariance matrix into signal and noise subspaces. It is shown to be robust to variations in the SNR and the number of data samples. Furthermore, it provides useful effective channel order estimates, leading to sufficiently good blind approximation/equalization of measured real-world microwave radio channels     

The channel capacity of a certain noisy timing channel

The effect of noise on a simple covert timing channel is investigated. Shannon's information theory is used to quantify the resulting information flow across the channel. In particular, how a probabilistic response time to a query by the receiver affects the mutual information and channel capacity is studied. The channel capacity is expressed in terms of the critical probability for the mutual information function which is given in closed form in terms of Wright's hypergeometric function     

Frequency-selective fading channel estimation with a polynomialtime-varying channel model

A rectangular-windowed least-squares estimator using a polynomial model of the time-varying channel taps is proposed for estimating the impulse response of a frequency-selective fading channel. This method provides a significant improvement in mean square identification error (MSIE) over the conventional least mean squares (LMS) and the exponentially weighted recursive least squares (EW-RLS) algorithms without a polynomial model. A detailed study of the effects of channel parameters, such as the fading rate and the signal-to-noise ratio, on the proposed method is carried out. The performance of the method depends on the window size of the least squares estimator and the polynomial order being used. Algorithms to obtain the approximately optimal window size and polynomial order are proposed and are shown to perform well     

基于FPGA的信道估计和信道均衡的实现

信道估计与信道均衡是自组网物理层关键技术;本文提出了一种改进的LMMSE信道估计算法;然后设计并实现了基于FPGA硬件平台的信道估计与均衡模块;结果表明,该模块算法复杂度低,占用硬件资源少,估计精度符合预期,满足应用需求.     

Ergodic channel capacity of the spatial correlated rayleigh MIMO channel

The theoretical capacity of the spatial correlated Rayleigh multiple input multiple output (MIMO) channel is an important issue in MIMO technology. In this article, an ergodic channel capacity formula of the spatial correlated rayleigh MIMO channel is provided, which is deduced when two antennas exist at either the transmitter or the receiver. The multi-dimensional least-squares fit algorithm is employed to narrow the difference between the theoretical formula capacity and the practical capacity. Simulation results show that the theoretical capacity approaches the practical one closely.     

A Hopfield neural-network-based dynamic channel allocation withhandoff channel reservation control

As channel allocation schemes become more complex and computationally demanding in cellular radio networks, alternative computational models that provide the means for faster processing time are becoming the topic of research interest. These computational models include knowledge-based algorithms, neural networks, and stochastic search techniques. This paper is concerned with the application of a Hopfield (1982) neural network (HNN) to dynamic channel allocation (DCA) and extends previous work that reports the performance of HNN in terms of new call blocking probability. We further model and examine the effect on performance of traffic mobility and the consequent intercell call handoff, which, under increasing load, can force call terminations with an adverse impact on the quality of service (QoS). To maintain the overall QoS, it is important that forced call terminations be kept to a minimum. For an HNN-based DCA, we have therefore modified the underlying model by formulating a new energy function to account for the overall channel allocation optimization, not only for new calls but also for handoff channel allocation resulting from traffic mobility. That is, both new call blocking and handoff call blocking probabilities are applied as a joint performance estimator. We refer to the enhanced model as HNN-DCA++. We have also considered a variation of the original technique based on a simple handoff priority scheme, here referred to as HNN-DCA+. The two neural DCA schemes together with the original model are evaluated under traffic mobility and their performance compared in terms of new-call blocking and handoff-call dropping probabilities. Results show that the HNN-DCA++ model performs favorably due to its embedded control for assisting handoff channel allocation     

Novel semi-blind channel estimation schemes for Rician fading MIMO channel

In this paper, we propose two novel semi-blind channel estimation techniques based on QR decomposition for Rician fading Multiple Input Multiple Output (MIMO) channel. In the first technique, the MIMO channel matrix H can be decomposed as an upper triangular matrix R and unitary rotation matrix Q as H = RQ. The matrix R is estimated blindly from only received data by using the orthogonal matrix triangularization based Householder QR decomposition, while the optimum rotation matrix Q is estimated exclusively from the algorithm of Orthogonal Pilot Maximum Likelihood Estimator (OPML) based on pilot information. In the second technique, the joint semi-blind channel and data estimation is performed by using the Least Square (LS) algorithm based on QR decomposition. The simulation results obtained for 4-PSK data modulation scheme using two transmitters and six receiver antennas for different Rice factor (K) have shown that the BER performance increases with an increase in the Rice factor. Finally, we compare these two new techniques with the conventional semi-blind channel estimation technique based on Whitening Rotation (WR), and the results show that the first proposed technique outperforms and the second technique achieves a very nearby performance as compared to the technique based on whitening rotation.     

一种降低复杂度的压缩感知水声信道估计方法

针对浅海的水声信道稀疏特性,提出了一种降低复杂度的前向回溯正交匹配追踪(RC-LABOMP)信道估计方法。首先计算正交匹配追踪和子空间追踪信道估计算法的两类支撑集,接着根据两类支撑集的交集和并集,预处理先验信息,最后利用先验信息完成前向回溯正交匹配追踪信道估计。该算法经过先验信息的预处理,能够减少原LABOMP算法的迭代次数,同时缩小原子的索引范围,因此能够显著降低原LABOMP算法的计算复杂度。此外,将提出的算法与水声Turbo均衡系统相结合,更适用于水声通信系统。仿真结果表明,所提算法在随机信道和水声信道条件下,具有估计精度高、误码率低的特点,同时能够显著降低LABOMP算法的计算复杂度,是一种适用于浅海水声信道的有效估计方法。