A Novel Detection Algorithm Efficient for Turbo Coded CDMA Signals in Detect and Forward Cooperative Channels

In this paper, a new detection algorithm is proposed for turbo coded Code Division Multiple Access (CDMA) signals in detect and forward cooperative channels. Use of user cooperation makes much improvement in the performance of CDMA systems. Due to the special structure of CDMA systems, cooperative schemes increase the sum and cutoff capacities of CDMA based wireless systems and improve the quality of user-partner link which enhances the overall performance of the system. In this paper, a new combining scheme is proposed that makes the receiver more robust against the decision errors in the partner link. This structure is simulated for punctured 1/2 rate 4 states turbo code in a channel with first order Markov time variation and different Rice factor variances. Through various simulations, it is shown when the channel estimates are available in the partner and receiver, the cooperation between users provides much diversity gain especially while using the new proposed combining algorithm.     

Blind identification of multipath channels: a parametric subspaceapproach

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered. Using the assumption of a specular multipath channel, we investigate a parametric variant of the so-called subspace method. Nonparametric subspace-based methods require precise estimation of the model order; overestimation of the model order leads to inconsistent channel estimates. We show that the parametric subspace method gives consistent channel estimates when only an upper bound of the channel order is known. A new algorithm, which exploits parametric information on the channel structure, is presented. A statistical performance analysis of the proposed parametric subspace criterion is presented; limited Monte Carlo experiments show that the proposed algorithm is second-order optimal for a large class of channels     

Blind OFDM Channel Estimation Using FIR Constraints: Reduced Complexity and Identifiability

In this correspondence, blind channel estimators exploiting finite alphabet constraints are discussed for orthogonal frequency-division multiplexing (OFDM) systems. Considering the channel and data jointly, a joint maximum-likelihood (JML) algorithm is described, along with identifiability conditions in the noise-free case. This approach enables development of general identifiability conditions for the minimum-distance (MD) finite alphabet blind algorithm of Zhou and Giannakis. Both the JML and MD algorithms suffer from high numerical complexity, as they rely on exhaustive search methods to resolve a large number of ambiguities. We present a substantially more efficient blind algorithm, the reduced complexity minimum distance (RMD) algorithm, by exploiting properties of the assumed finite-length impulse response (FIR) channel. The RMD algorithm exploits constraints on the unwrapped phase of FIR systems and results in significant reductions in numerical complexity over existing methods. In many cases, the RMD approach is able to completely eliminate the exhaustive search of the JML and MD approaches, while providing channel estimates of the same quality     

智能天线系统中上行多用户相干信号的分离与DOA估计方法研究

本文研究了平主道中的窨特征估计问题。提出了一种工于循环累量的多用户空间牲针该算法用于智能天线中的ＳＤＭＡ实现。该算法构造了循环累量域四,通过对空间特征循环矩阵的特征分解可以得到用户信号的空间特征估计。并以此为基础,设计了用于上行多波束成形的窨滤波器组实现了上行多用户信号的分离,利用空间滤波器的空间频率响应确定共信道多用户信号的ＤＯＡ《     

A MIMO turbo equalizer for frequency-selective channels with unknown interference

A new space-time turbo equalization algorithm is derived for frequency-selective multiple-input-multiple-output (MIMO) channels with unknown interference. The algorithm is an extension of our proposed MIMO equalization algorithm , which performs joint channel estimation, multiple users' signal detection, and decoding, all in an iterative manner. This paper's proposed algorithm uses estimates of the correlation matrix of composite unknown interference-plus-noise components to suppress the unknown interference while effectively separating multiple users' signals to be detected (referred to as "known user" later). The correlation matrix of the composite unknown interference-plus-noise components can be estimated by time averaging the instantaneous empirical correlation matrix over the training period. Since the iterative channel estimation yields better channel estimates as more iterations are performed, thereby the estimate of the correlation matrix of the unknown interference-plus-noise components also becomes more accurate. This results in better signal detection performances, even in the presence of unknown interferers. A series of computer simulations show that this paper's proposed algorithm can properly separate known users' signals while suppressing unknown interference.     

基于反向链路状态的动态优化信道分配

本文提出了一种基于反向链路载干比的信道分配优化模型.这种模型较原有的兼容矩阵模型更接近实际系统,并且能够应用于分析干扰自适应信道分配方案.本文在提出这种模型的同时,对比了该模型和原有模型在描述信道分配问题的精度和能力上的优劣.此外,本文还提出了使用改进遗传算法求解该模型下信道分配问题的方法.分析和实验均说明本文提出的模型对实际环境进行了更精确的描述,通过使用这种模型求解信道分配问题能够更好利用信道资源.     

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.     

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.     

Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.      

Transmitter redundancy for blind estimation and equalization oftime- and frequency-selective channels

Joint mitigation of time- and frequency-selective fading is an important and challenging problem in mobile communications. Relying on transmitter-induced redundancy, we propose novel channel estimation and symbol recovery approaches for blind identification and equalization of time- and frequency-selective channels, where the time variation is modeled deterministically by a basis expansion. The resulting statistical algorithm enables the usage of a single antenna, dispenses with channel disparity conditions of existing approaches, and allows channel order overestimation. In addition, new deterministic algorithms for generalized OFDM systems are introduced that produce reliable estimates with few data points at high SNR's. Simulations illustrate the approaches developed     

A least-squares approach to blind channel identification

Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented     

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.     

OFDM系统中基于恒模的盲信道估计算法

该文提出了一种适用于采用PSK调制方式的正交频分复用(OFDM)系统的准盲信道估计算法。该算法首先利用系统信号的恒模特性得到有限个可能的信道,然后利用信号的有限字符特性从可能信道中寻找出最佳信道,因而具有较低的计算复杂度。与现有的盲信道估计算法不同,该算法利用二阶统计量而不是高阶统计量估计信道,获得了较好的估计性能。仿真结果表明,该算法的性能优于基于有限字符特性的盲信道估计算法。     

Joint Angle and Delay Estimation (JADE) in Antenna Array CDMA Systems

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一种用于低速卫星通信系统的校频算法

提出一种适用于有广播信道的低速卫星通信系统校频算法。在该算法中,各地球站首先对接收网管广播信道的载波频差进行估计,然后在此基础上估计各站参考源频率及卫星转发器的本振频率,最后再根据估计结果对发射载波频率或收载波频率进行开环补偿,以实现通信信号的快速同步。数学分析和试用结果表明,该算法可以解决有广播信道的低速突发卫星信号的快捕问题。     

Pilot Optimization for Structured Compressive Sensing Based Channel Estimation in Large-Scale MIMO Systems with Superimposed Pilot Pattern

Compressive sensing (CS) has attracted much attention in wireless communications due to its ability to attain acceptable channel estimates with a small number of pilots. To further reduce the pilot overhead in multi-input multi-output (MIMO) systems, CS-based channel estimation may employ superimposed pilot pattern. Previous works on superimposed pilot design generally allocate pilots randomly, which may give ill-posed measurement matrices. In this paper, we focus on deterministic pilot allocation for large-scale MIMO systems with superimposed pilot pattern to improve the performance of structured CS based channel estimation. By exploiting the spatial common sparsity and the error bound of block sparse reconstruction, a new criterion is firstly proposed to optimize the pilots in the Hadamard space. The proposed criterion makes full use of the information about the principal angles across the blocks in the measurement matrix, which can enhance the average recovery ability and exclude the worst pilots simultaneously. Secondly, a genetic algorithm is proposed to minimize the merit factor of the proposed criterion efficiently. Simulation results show that the proposed optimized pilots outperform the random pilots in terms of mean-squared error by about 3 dB. Moreover, the proposed criterion is more likely to achieve better measurement matrices than the traditional criteria.     

Symbol Detection of IDMA Systems in the Presence of Carrier Frequency Offsets

In this paper we investigate the detection algorithms for interleave division multiple access (IDMA) systems in the presence of carrier frequency offsets (CFOs). The existing IDMA detection algorithm is designed under the zero CFO assumption and its performance will be degraded when the CFOs are present. We first extend the existing algorithm to the nonzero CFO case by utilizing effective channel coefficients which take the CFO effects into account. Then we turn to a more practical scenario with imperfect CFO estimates. We propose an algorithm that can cope with the residual CFO effects by integrating the CFO updating into the iterative receiver. Signal-to-interference-plus-noise ratio analysis and simulations show the feasibility and superiority of our proposed algorithms.     

一种新的OFDM联合信道估计方法

OFDM（正交频分复用）作为一种高效可靠的传输方式,被广泛应用于无线通信系统中,近年来越来越受到人们的关注。文中主要研究基于导频的OFDM信道估计,在此基础上提出了一种采用最小二乘法的联合信道估计方法。这种方法能够将传送数据与导频结合起来进行更精确的信道估计,从而提高系统性能。仿真结果表明,文中方法优于经典的信道估计方法。     

Iterative multiuser receivers for CDMA channels: an EM-basedapproach

Maximum-likelihood detection for the multiuser code-division multiple-access (CDMA) channel is prohibitively complex. This paper considers new iterative multiuser receivers based on the expectation-maximization (EM) algorithm and related, more powerful “space-alternating” algorithms. The latter algorithms include the SAGE algorithm and a new “missing parameter” space-alternating algorithm that alternately updates individual parameter components or treats them as probabilistic missing data. Application of these EM-based algorithms to the problem of discrete parameter estimation (i.e., data detection) in the Gaussian multiple-access channel leads to a variety of convergent receiver structures that incorporate soft-decision feedback for interference cancellation and/or sequential updating of iterative bit estimates. Convergence and performance analyses are based on well-known properties of the EM algorithm and on numerical simulation     

超宽带系统中同步和信道参数的闭式估计算法

提出超宽带系统中同步和信道参数的新型估计算法。首先对接收信号进行傅立叶变换,由傅立叶变换系数构造两个矩阵,把信道时延估计转化成矩阵束的特征值求解问题。在求解出时延后再进一步求解信道参数。算法的最大特点是能得到多径时延和多径增益的闭式解,避免现有算法普遍存在的搜索和迭代求解。