Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO–OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR.     

Optimal diversity combining based on linear estimation of rician fading channels

Optimal receiver diversity combining employing linear channel estimation is examined. Based on the statistical properties of least-squares (LS) and minimum mean square error (MMSE) channel estimation, an optimal diversity receiver for wireless systems employing practical linear channel estimation on Rician fading channels is proposed. The new receiver structure includes the conventional maximal ratio combining receiver as a special case. Exact analytical expressions for the symbol error rates (SERs) of LS and MMSE channel estimation aided optimal diversity combining are derived. It is shown that, if an optimal detector is used, an MPSK wireless system with MMSE channel estimation has the same SER when the MMSE channel estimation is replaced by LS estimation. This is an interesting counterexample to the common perception that channel estimation with smaller mean square error leads to smaller SER. Extensive simulation results validate the theoretical results.     

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.     

Training sequence based channel estimation for indoor visible light communication system

Channel estimation is a key technology in indoor wireless visible light communications(VLCs).Using the training sequence(TS),this paper investigates the channel estimation in indoor wireless visible light communications.Based on the propagation and signal modulation characteristics of visible light,a link model for the indoor wireless visible light communications is established.Using the model,three channel estimation methods,i.e.,the correlation method,the least square(LS) method and the minimum mean square error(MMSE) method,are proposed.Moreover,the performances of the proposed three methods are evaluated by computer simulation.The results show that the performance of the correlation method is the worst,the LS method is suitable for higher signal to noise ratio(SNR),and the MMSE method obtains the best performance at the expense of highest complexity.     

Channel estimation in ACO-OFDM employing different transforms for VLC

The pronounced eminency of assuring simultaneous illumination and communication has driven Visible Light Communication (VLC) to gain significant ubiquity in recent times. This paper proposes comb type pilot arrangement based channel estimation for Asymmetrically Clipped Optical OFDM (ACO-OFDM) and different multicarrier transmission systems like Discrete Hartley Transform (DHT)-based ACO-OFDM and Fast-Walsh Hadamard transform (FWHT)-based Hadamard Coded Modulation (HCM) over dispersive VLC channel. Various channel estimation algorithms like Least Square (LS), Minimum Mean Square Error (MMSE), and Interpolation techniques namely Linear, Spline, and Low-Pass are evaluated and compared for the aforesaid systems employing different orders of constellation. Here, an elaborate mathematical analysis is accomplished and Cramer Rao Lower Bound (CRLB) is derived for the channel estimation error. Simulated results emphasize that, ACO-OFDM and DHT-based ACO-OFDM have improved Bit Error Rate (BER) performance than HCM at lower Signal to Noise Ratio (SNR), while at higher SNRs HCM dominates the former. Furthermore, the simulated results evidences that, in all multicarrier systems MMSE algorithm has reducible probability of error than LS because, at higher SNRs LS is more susceptible to noise and Spline Interpolation outperforms both LS and MMSE. The simulated results are validated analytically demonstrating good agreement.     

一种改进的基于导频的OFDM信道估计算法

文中对OFDM（正交频分复用）系统中基于导频的信道估计技术进行了研究。MMSE（最小均方误差估计）算法有很好的性能但复杂度大,LS（最小二乘法估计）算法复杂度低但性能受到限制,在综合考虑性能和复杂度的情况下介绍了两种改进方法。并针对基于梳状导频的二项插值算法的缺陷,提出了一种改进的算法,计算机仿真证明了改进算法的误码率性能在高信噪比条件下高于原算法。     

MIMO信道估计在对流层散射中的性能分析

将MIMO与对流层散射通信相结合。首先阐述了对流层散射信道的衰落特性,建立了MIMO对流层散射信道的信道模型,并给出了具体的仿真步骤。然后,根据散射信道的特性研究了MIMO基于训练序列的信道估计,重点分析了最小二乘(LS)和最小均方误差(MMSE)估计算法,并针对不同算法的性能进行了仿真分析比较。结果表明,在对流层散射通信中,良好的信道估计能使发送数据在接收端被正确地恢复接收,提高了系统性能。     

信道估计在OFDM中的应用

介绍了信道估计在OFDM中的应用,提出一种易于实现的MMSE算法,并对MMSE和LS的性能进行了比较,同时研究了它们的改进算法。结果表明改进算法有很好的性能,可以逼近理想估计。在系统的实现中有较好的参考价值。     

基于空时分组训练序列的低复杂度MIMO-OFDM信道估计

为提高MIMO-OFDM系统信道估计的性能,首先通过分析揭示了基于空时分组训练序列的MMSE信道估计器和子空间投影之间的关系。在此基础上,对空时分组训练序列简单得到的LS信道估计器进行子空间投影,得到了复杂度较低同时性能良好的信道估计。为进一步提高性能,最后提出了一种基于FFT的多径时延估计方法,使用该方法可以得到更精确的信道响应信号子空间,从而提高投影估计的性能。计算机仿真证明了所提出算法的有效性。     

一种新的最小均方误差线性合并算法

针对未编码的多输入多输出系统,将基于训练序列的最小均方误差(MMSE)信道估计算法与最优线性无偏估计结构(BLUE)相结合对已估计的信道参数进行估计.仿真结果表明,使用线性合并的MMSE算法比传统的MMSE算法具有较小的参数估计误差,比使用线性合并的LS算法性能更好.     

Training Signal Design for Estimation of Correlated MIMO Channels With Colored Interference

In this paper, we study the problem of estimating correlated multiple-input multiple-output (MIMO) channels in the presence of colored interference. The linear minimum mean square error (MMSE) channel estimator is derived and the optimal training sequences are designed based on the MSE of channel estimation. We propose an algorithm to estimate the long-term channel statistics needed for the construction of the optimal training sequences. We also design an efficient scheme to feed back the required information to the transmitter where we can approximately construct the optimal sequences. Numerical results show that the optimal training sequences provide substantial performance gain for channel estimation when compared with other training sequences     

Efficient Channel Estimation for MIMO Single-Carrier Block Transmission With Dual Cyclic Timeslot Structure

We investigate channel estimation for timeslot-structured single-carrier block transmission (SCBT) over space-, time-, and frequency-selective fading multiple-input multiple-output (MIMO) channels. A MIMO-SCBT with a dual cyclic timeslot structure is presented first. Then, an optimal channel estimation in the minimal mean square error (MMSE) sense on the timeslot basis is investigated. It is shown that the optimal pilots for the timeslot-based MMSE channel estimation are related to the statistical channel state information in eigenmode. Under the assumption that the transmit correlation is unknown at the transmitter, the optimal pilots satisfy the same condition as reported for the block-based least-square (LS) channel estimation in literature, and the channel estimation can be simplified to initial block-based LS channel estimation followed by space-time postprocessing. Particularly, for spatially uncorrelated channels, the space-time postprocessing can be reduced to pathwise processing. A new design of the pilot sequences is given, which leads to an efficient implementation of the channel estimation. Later on, a more efficient implementation for the initial channel estimation is obtained by using the structure of the pilot sequences, and discrete cosine transform (DCT)-based implementation is developed for the space-time postprocessing to approximate the optimal solution with low implementation complexity. Finally, the performance of the proposed channel estimation is verified via simulations.     

Hybrid SNR-Adaptive Multiuser Detectors for SDMA-OFDM Systems

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.     

Least Square Channel Estimation for Two‐Way Relay MIMO OFDM Systems

This letter considers the channel estimation for two‐way relay MIMO OFDM systems. A least square (LS) channel estimation algorithm under block‐based training is proposed. The mean square error (MSE) of the LS channel estimate is computed, and the optimal training sequences with respect to this MSE are derived. Some numerical examples are presented to evaluate the performance of the proposed channel estimation method.     

Performance Analysis of Tikhonov Regularized LS Channel Estimation for MIMO OFDM Systems with Virtual Carriers

In this paper the effect of virtual carriers (VCs) on channel estimation performance is considered for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The link between the number of VCs and the inverse problem in a conventional LS channel estimation is identified. It is observed that a linear increase in VCs contributes to an exponential increase of the system inverse problem as the condition number of the inverse matrix is exponentially increased. A solution is proposed using a low complexity modified LS channel estimation. The performance analysis of the modified LS channel estimation, regarding the effect of VCs, show gains in both the mean square error (MSE) performance of channel estimation as well as bit error rate (BER) system performance compared to conventional LS. The performance is improved irrespective of the increasing number of VCs as the estimator is shown to be insensitive to the increase of VCs in the OFDM system. The complexity reduction is also considered and an approach is proposed showing a similar complexity as LS.     

Pilot-Aided Angle-Domain Channel Estimation Techniques for MIMO-OFDM Systems

Early multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation techniques treat channels as spatially uncorrelated. However, in many situations, MIMO-OFDM channels tend to be spatially correlated, for example, due to limited scattering. For such channels, estimation performance can be improved through exploitation of prior knowledge of the channel spatial correlation, for example, by means of the linear multiple mean square error (MMSE) technique. This knowledge is, however, not always available. As an alternative, we investigate techniques in the angle domain, where the MIMO-OFDM channel model lends itself to a physical interpretation. Our theoretical analysis and simulation results indicate that the proposed angle-domain approximated MMSE (AMMSE) channel estimation technique performs well in terms of the mean square error (mse) for various channel models representing different indoor environments. When a suitable threshold is chosen, we can use the angle-domain most-significant-taps selection technique instead of the angle-domain AMMSE technique to simplify the channel estimation procedure with little performance loss.     

Iterative near–far resistant channel estimation by using a linear minimum mean squared error detector

Channel estimation techniques for CDMA system need to combat multiple access interference (MAI) to improve the estimation performance. The linear MMSE detector has certain advantages with respect to the near–far problem and can be used to develop a channel estimation algorithm. In this paper, an efficient iterative method for near–far resistant single-user mobile radio channel estimation in slow fading multi-path direct sequence code division multiple access (DS-CDMA) channels is presented. Computer simulation results demonstrate that a significant performance improvement can be achieved with the proposed method especially under extreme near–far conditions.     

基于OFDM的无线通信系统信道估计技术研究

OFDM是第四代移动通信系统的核心技术之一,信道估计是OFDM中急需解决的一个关键技术。首先介绍了OFDM技术的基本原理及其系统组成,重点研究了基于导频的OFDM信道估计技术,针对块状导频插入方式,讨论了LS（最小二乘）、MMSE（最小均方误差）和SVD（奇异值分解）算法,并通过仿真对其性能进行了比较。结果表明：MMSE和SVD算法比LS算法有更好的性能,但是LS算法的复杂度较低。     

OFDM系统中的信道估计算法比较

研究了OFDM系统中基于LS、MMSE及SVD的信道估计算法,并对其进行了性能分析和比较.通过SVD降低了运算复杂度,简化了MMSE算法,且性能良好.     

OFDM-MIMO系统中发射分集和信道估计技术研究

文章研究了OFDM-MIMO系统中的循环延迟分集（CDD）,以及基于Alamouti的空时分组编码（STBC）与空频分组编码（SFBC）,针对发射分集方案所必须知道的信道信息,对两种信道估计方法,即最小二乘（LS）＋线性插值与最小均方误差（MMSE）插值进行了研究。通过链路仿真还给出了以上分集方案在不同信道条件下的性能曲线,此外,对两种信道估计方法的误帧率做了统计。结果表明,CDD结合信道编译码可获得分集增益,有效对抗无线信道的衰落性。STBC,SFBC在不需要外码的情况下便可获得较大分集增益,且分别适用于频率选择性衰落与时间选择性衰落信道。而MMSE估计可以获得较为理想的误帧率,为分集方案提供较准确的信道参数以提高系统的可靠性。