Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems

Multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to single-input-single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell layered space-time architecture (BLAST) or space division multiple access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a "rank-deficient" scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the forward error correction (FEC) or channel decoder, which inevitably limits the system's achievable performance. By contrast, our proposed GA is capable of providing "soft" outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme.     

Channel Estimation by Using Short Training Sequences in CDMA Systems

Multiuser detection techniques are known to be effective to counter the presence of multiuser interference in code division multiple access channels. Multiuser detectors can provide excellent performance only when the channel impulse responses of all the users are precisely known. Hence, channel estimation becomes a challenging issue in mobile communication systems. In this paper, we address the problem of efficient maximum likelihood mobile radio channel estimation at high channel efficiency that requires a short training sequence along with the known spreading sequence. The proposed system can be employed in both the uplink and downlink of a heavily loaded multiuser CDMA system. The extension of the approach with unknown users' delays are also proposed. We present results that show the success of this method in recovering the transmitted bits with a relatively small number of preamble bits. Ahmet Rizaner was born in Larnaca, Cyprus, on January 31, 1974. He received the B.S. and M.S. degrees in Electrical and Electronics Engineering from the Eastern Mediterranean University, Famagusta, North Cyprus, in 1996 and 1998, respectively. He completed his PhD. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His main research interests include CDMA communications, adaptive channel estimation, and multiuser detection techniques. Hasan Amca was born in 1961 in Nicosia-Cyprus. He graduated from the Higher Technological Institute in Magosa-Cyprus (which is renamed later as Eastern Mediterranean University). He joined EMU in 1985 after receiving a M.Sc. (Digital Signal Processing) degree from the University of Essex in England (1985). He took his Ph.D. (Mobile Communications) from the University of Bradford where he was on a Commonwealth scholarship. He has been teaching in the Electrical and Electronic Engineering Department of Eastern Mediterranean University since 1993 where he also served as the vice chairman from Spring 1998 to Spring 2000. He has been appointed as the Director of the School of Computing and Technology of the EMU since Spring 2000. His research interests include Multi User Detection of CDMA signals, Adaptive Equalisation, Multi Carrier Systems, Mobile Radio Systems and Networks, Internet and Information Technology Applications in Education. Kadri Hacıoğlu was born in Nicosia, Cyprus. He received the B.Sc., M.Sc., and Ph.D. degrees in electrical and electronic engineering from the Middle East Technical University, Ankara, Turkey, in 1980, 1984, and 1990, respectively. After his two-year military service, in 1992, he joined the faculty of Eastern Mediterranean University, Magosa, North Cyprus, as an Assistant Professor, and became an Associate Professor in 1997. While there, he taught several classes on electronics, digital communications, speech processing and neural networks. During this time, he conducted research on applying fuzzy logic, neural networks, and genetic algorithms to signal processing and communications problems. From 1998 to 2000, he was a Visiting Professor in the Department of Computer Science, University of Colorado, Boulder. Here, he taught classes on neural networks and continued his research. Since 2000, he has been a Research Associate at the Center for Spoken Language Research, University of Colorado. He has authored or coauthored numerous papers and supervised a dozen M.Sc./Ph.D. theses. His current research interests are concept-based language modeling, speech understanding, natural language generation, and search methods in speech recognition/understanding. He also does research on multiuser detection and equalization in CDMA systems. Ali Hakan Ulusoy was born in Eskişehir, Turkey, on June 3, 1974. He graduated from the double major program of the department of Electrical and Electronic Engineering and department of Physics in Eastern Mediterranean University as the first rank student of Faculty of Engineering in 1996. He received his M.S. degree in Electrical and Electronic Engineering in Eastern Mediterranean University in 1998. He completed his PhD. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His current research interests include receiver design, multi-user detection techniques, blind and trained channel estimation in Code Division Multiple Access (CDMA).     

叠加训练序列OFDM系统的一种迭代信道估计方法

在瑞利多径衰落信道中，针对叠加训练序列正交频分复用系统的迭代接收机，提出了一种迭代信道估计方法．在迭代译码开始前，利用与数据符号同时发送的训序列得到信道估计初始值，并在每次迭代后利用更新的码字比特先验信息估计接收信号中的数据符号，计算更准确的信道估计值．理论分析给出了每次迭代后的信道估计值的均方误差性能．在多径衰落信道中，通过计算机仿真验证了该迭代信道估计方法的有效性和可靠性。     

Frequency-Offset Estimation for MIMO and OFDM Systems Using Orthogonal Training Sequences

We propose a training-sequence-based frequency-offset estimator for a multiple transmit-and-receive antenna system in frequency-flat fading channels. The estimator is based on the maximum likelihood (ML) criterion and does not require channel information. To reduce the computational load, they propose to use special training sequences-the periodic orthogonal codes. Using these codes, we get a closed form estimator which requires much lower computational load (some additions and multiplications). For the high signal-to-noise ratio and small frequency offset, the proposed estimator achieves the performance of the optimal ML estimator, which locates the peak of the likelihood function. We also apply the proposed estimator to a multiple antenna system in frequency-selective channels and an orthogonal-frequency-division-multiplexing system. With theoretical analysis and simulations, we evaluate the performance of the proposed estimator     

Time-varying Channel Estimation and Symbol Detection Using Superimposed Training in OFDM Systems

In mobile orthogonal frequency division multiplexing (OFDM) systems, time-varying channels result in severe intercarrier interference (ICI), and greatly degrade the system performance. So, it is necessary to estimate the accurate channel for equalization of received symbols. But, the conventional pilot-assisted channel estimation scheme consumes valuable bandwidth. In this paper, we adopt superimposed training approach for OFDM systems to estimate the time-varying channel, which is approximated by a basis expansion model (BEM). The proposed scheme is an extension of the superimposed training approach previously proposed for time-invariant channels in OFDM systems. At the same time, we employ an iterative best linear unbiased estimator (BLUE) to minimize the mean square error (MSE) of the coefficient estimates and improve the system performance. Simulation results prove the effectiveness of the proposed scheme in fast time-varying scenario.

基于隐训练序列的MIMO信道估计

提出一种基于隐训练序列的MIMO信道估计模型,模型信道估计方法不占用额外的信号带宽和具有较高的估计精度.采用最小二乘(LS)方法,推导出信道估计误差的均方差和信道容量的下限.数值模拟结果证实在相同条件下,采用隐训练序列要比直接采用训练序列对系统容量的改善5 dB左右,并且也验证一些有用的结论:不恰当的天线数目不能提高信道容量.     

一种OFDM系统中叠加导频的信道估计方法

提出了一种新的基于叠加导频的信道估计算法。利用循环序列频域能量集中在某些频点上的特点,消除未知传输数据对导频的影响,将导频完全从接收数据中分离出来。在此基础之上,结合PN序列的自相关特性,在时域进行信道估计,进一步降低了噪声对导频的影响。仿真结果表明,与传统方法相比,该方法不但误码率和均方误差更低,而且还具有计算复杂度低、频带利用率高的特点。     

Iterative Soft-Kalman Filter-based Data Detection and Channel Estimation for Turbo Coded MIMO–OFDM Systems

MIMO channels are often assumed to be constant over a block or packet. This assumption of block stationarity is valid for many fixed wireless scenarios. However, for communications in a mobile environment, the stationarity assumption will result in considerable performance degradation. In this paper, we focus on a new channel estimation technique for Turbo coded MIMO systems using OFDM. In the proposed MIMO–OFDM system, pilots are placed on selected subcarriers and used by a pair of Kalman filter (KF) channel estimators at the receiver. The KF channel estimates are then utilized by a MIMO–OFDM soft data detector based on the computationally efficient QRD-M algorithm. The soft detector output is fed back to the Kalman filters to iteratively improve the channel estimates. The extrinsic information generated by the Turbo decoder is also used as a priori information for the soft data detector. The overall receiver thus combines MIMO data detection, KF-based channel estimation, and Turbo decoding in a joint iterative structure yielding computational efficiency and improved bit-error rate (BER) performance. Parts of this paper were presented at ICC’2005, Seoul, Korea. This work was supported in part by NSF Grant No. CCF-0429596. This work was done when he was with the Nokia Research Center in Dallas, USA.     

利用ZC序列的OFDM同步及稀疏信道估计

利用ZC序列构造了新导频符号,能够在时域兼具完成OFDM符号定时同步,频偏估计及稀疏信道估计。符号定时同步无需本地序列,频偏估计在时域完成不需要进行FFT,稀疏信道估计则选取优化的由ZC序列形成的感知矩阵对时域信道进行感知,并以OMP算法重构。仿真结果表明,新方法能在时域以较高的精度完成同步和稀疏信道估计。     

Iterative Channel Estimators in V-BLAST OFDM Systems

An iterative pilot-symbol aided modulation (PSAM) channel estimation approach is proposed for vertical Bell Laboratories layered space-time (V-BLAST) orthogonal frequency division multiplexing systems operating on frequency-selective fading channels. Since the signals at the receive antennas are the superposition of signals from multiple transmit antennas, accurate channel estimates are crucial for good error performance. Furthermore, the time selectivity of the fading channels leads to inter-carrier interference (ICI). While ICI can be ignored for slow fading channels, it should be mitigated for fast fading channels. This paper proposes an ICI mitigation scheme for time-varying channels. We also propose an iterative channel estimator with low-complexity. Simulation results demonstrate the usefulness of the proposed algorithm on frequency-selective fading channels.     

Iterative Carrier Frequency Offset and Channel Estimation for Underwater Acoustic OFDM Systems

This paper presents a practical low-density paritycheck (LDPC) coded OFDM system designed for the underwater acoustic channel with its attendant sparse multipath channel and Doppler effects. The carrier frequency offset (CFO) and channel state information (CSI) are assumed unavailable to both to the transmitter and the receiver. Several different receiver structures are considered, all of which perform CFO/channel estimation, detection and decoding in an iterative manner. The convergence behavior of the iterative receivers and their asymptotic performance are evaluated using the extrinsic information transfer (EXIT) chart method. OFDM receiver performance is further evaluated through simulations and field tests in shallow water.     

OFDM系统迭代信道估计初始值预测的改进算法

为了得到更精确的迭代信道估计中的信道初始频响值,在基于二次曲线拟合预测的OFDM系统联合迭代信道估计与符号检测算法的基础上,提出一种基于MMSE准则的加窗线性预测方法对信道的初始频率响应值进行估计.仿真结果表明,在较高信噪比下,文中提出的基于MMSE准则的加窗线性预测的估计方法所得到的系统误码性能明显优于采用二次曲线拟合预测的信道估计方法.在复杂度增加不大的情况下,该方法是一种有效的改进算法.     

基于正交训练序列的MIMO系统联合最大似然时频同步和信道估计

该文推导了多输入多输出(MIMO)系统中的符号定时、频偏和信道参数的联合最大似然(ML)估计。针对联合ML估计没有闭合的表达式、数值计算复杂度高的问题,该文提出了一种基于重复结构的正交训练序列的简化估计算法。该估计算法形式简单、复杂度低,且仍为最大似然估计。最后仿真分析了最大似然参数估计的均方误差与接收信噪比和天线数目的关系,并与Cramer-Rao界作了比较,表明了该算法的有效性。     

OFDM系统中的盲信道估计

本文从OFDM信号的矩阵表示出发,分析比较了OFDM系统中现有的各种盲信道估计方法。OFDM盲信道估计方法分为两类,一类是统计型方法,它利用了发送信号和接收信号的统计特性;另一类是确定型方法,它利用了发送调制信号的固有特性。一般而言,统计型方法的计算量较小,但是估计精度不高且估计的实时性不好;而确定型方法的估计精度较高,实时性较好,但是其计算量较大。计算机仿真表明,这些盲信道估计方法的性能受信道参数尤其是多普勒频率影响很大,盲信道估计的实用化有待进一步研究。     

OFDM系统中一种新的迭代信道估计算法

本文提出了OFDM系统中一种新的基于软信息迭代处理的信道估计算法。该算法将面向判决最小二乘估计算法和盲估计算法相结合,在估计器中构造了一种新的置信度量函数,根据解码和软映射重构的反馈信号置信度大小在两种估计算法中自适应选择,这样估计的信道频响可以有效提高软信息迭代接收性能,大大降低信道估计训练开销。仿真结果表明,本文提出的算法能有效跟踪信道时变,限制传统面向判决估计的错误传播,达到好的系统性能。     

叠加弱能量训练序列信道估计方法

本文提出了一种利用叠加弱能量的周期训练序列进行信道估计的线性最小均方误差(LMMSEE)算法.该方法不需要信道先验信息、不占用宝贵的带宽资源、计算量比常规LS方法更低.理论分析和计算机仿真表明:在训练序列周期比信道冲击响应长度大时,在较低的信噪比下,利用LMMSEE方法估计信道性能比LS方法更佳.     

基于DCT的OFDM信道估计

在宽带移动环境下,由于多普勒频移以及多径的影响,信道是时变的。为了消除信道对传输信号的影响,需要对信道进行估计。给出了2种基于离散余弦变换(DCT)的信道估计方法,可以很好的降低频谱泄露造成的混叠失真,适用于非整数倍的抽样信道。其中一种性能较好,另一种实现更加方便。仿真结果表明,这两种算法的性能均优于基于DFT的信道估计算法。     

OFDM系统信道估计算法研究

在OFDM技术应用于移动通信领域时,其信道估计技术是一项关键技术。分析了级联的一维的信道估计方法,采用加窗的方式改进了基于FFT一维级联信道估计算法,在此基础上提出一种新的基于DCT（EIDCT）变换的信道估计算法。最后给出了改进算法的性能仿真,并对仿真结果进行分析。     

Joint Semi-Blind Channel Estimation and Data Sequence Detection of OFDM Systems

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New Channel Estimation Method Using Singular Spectrum Analysis for OFDM Systems

Wireless Personal Communications - This paper presents a new method for OFDM channel estimation (CE) using singular spectrum analysis (SSA). In this method, the conventional LMMSE procedure is used...