Training-based and semiblind channel estimation for MIMO systems with maximum ratio transmission

This paper is a comparative study of training-based and semiblind multiple-input multiple-output (MIMO) flat-fading channel estimation schemes when the transmitter employs maximum ratio transmission (MRT). We present two competing schemes for estimating the transmit and receive beamforming vectors of the channel matrix: a training-based conventional least-squares estimation (CLSE) scheme and a closed-form semiblind (CFSB) scheme that employs training followed by information-bearing spectrally white data symbols. Employing matrix perturbation theory, we develop expressions for the mean-square error (MSE) in the beamforming vector, the average received signal-to-noise ratio (SNR) and the symbol error rate (SER) performance of both the semiblind and the conventional schemes. Finally, we describe a weighted linear combiner of the CFSB and CLSE estimates for additional improvement in performance. The analytical results are verified through Monte Carlo simulations.     

Transmit signal design for optimal estimation of correlated MIMO channels

We address optimal estimation of correlated multiple-input multiple-output (MIMO) channels using pilot signals, assuming knowledge of the second-order channel statistics at the transmitter. Assuming a block fading channel model and minimum mean square error (MMSE) estimation at the receiver, we design the transmitted signal to optimize two criteria: MMSE and the conditional mutual information between the MIMO channel and the received signal. Our analysis is based on the recently proposed virtual channel representation, which corresponds to beamforming in fixed virtual directions and exposes the structure and the true degrees of freedom in the correlated channel. However, our design framework is applicable to more general channel models, which include known channel models, such as the transmit and receive correlated model, as special cases. We show that optimal signaling is in a block form, where the block length depends on the signal-to-noise ratio (SNR) as well as the channel correlation matrix. The block signal corresponds to transmitting beams in successive symbol intervals along fixed virtual transmit angles, whose powers are determined by (nonidentical) water filling solutions based on the optimization criteria. Our analysis shows that these water filling solutions identify exactly which virtual transmit angles are important for channel estimation. In particular, at low SNR, the block length reduces to one, and all the power is transmitted on the beam corresponding to the strongest transmit angle, whereas at high SNR, the block length has a maximum length equal to the number of active virtual transmit angles, and the power is assigned equally to all active transmit angles. Consequently, from a channel estimation viewpoint, a faster fading rate can be tolerated at low SNRs relative to higher SNRs.     

Performance Comparison of MIMO-STBC Systems with Adaptive Semiblind Channel Estimation Scheme

Wireless communication is now a part of everyday life in the urban areas. Wireless LAN is mostly utilized communication system as an example. These wireless devices are data rate and range limited, for which the scientists are spending great efforts on finding ways to overcome these limitations. Multi input multi output (MIMO) antenna systems are the example through which these limitations have been reduced upto great extent which provides multilayer beamforming, diversity, and spatial multiplexing. Analysis of adaptive semiblind channel estimation scheme for MIMO antenna array systems with different code rate space time block coding (STBC) has been performed using the adaptive pilot assisted modulation scheme proposed earlier. Semi blind channel estimation method provides the best trade-off in terms of bandwidth overhead, computational complexity and latency. The result after using MIMO systems shows higher data rate and longer transmit range without any requirement of additional bandwidth or transmit power. This paper presents the detailed analysis of diversity coding techniques using MIMO antenna systems. Different STBC schemes have been explored and analyzed with the different code rate STBC using MATLAB environment and the simulated results have been compared in the semiblind environment which shows the improvement even in highly correlated antenna arrays, and is found close to the condition when channel state information is known to the channel.     

Semiblind channel estimation for CDMA systems with parallel data and pilot signals

Semiblind channel estimation combines the methods of channel estimation based on a pilot signal and blind channel estimation based on a data-only conveying signal. Maximum-likelihood (ML)-based semiblind estimators with Gaussian assumptions can provide improvement in performance, compared with channel-estimation schemes using the pilot signal only. This improvement can be even larger when the pilot and the data signals are sent simultaneously, as is the case in the third-generation wideband code-division multiple-access standards. However, the Gaussian ML approach results in very large complexity. Previously proposed semiblind methods with low complexity have been derived for serial pilot and data transmission, and are not suitable for the parallel transmission case. In this paper, algorithms for semiblind channel estimation for the parallel data and training signal case are developed. Approximations which reduce the computational complexity of the Gaussian ML method significantly are proposed. Solutions with iterations with very low attendant complexity are provided. The mean squared error analysis of the proposed method is obtained and compared with that of a method with no approximations. The approximations are justified through simulations, and the performance improvement over estimation schemes using the pilot signal solely is verified.     

双向MIMO中继网络中采用信道独立预编码的自干扰消除技术

双向中继网络在提高频谱效率的同时会引入额外的自干扰,本文针对放大转发（AF）模式下双向多输入多输出（MIMO）中继网络中的自干扰抵消问题,从消除信道估计误差引入的剩余自干扰着手,提出一种采用信道独立预编码的盲干扰抵消（BIC）方案。新方案在源节点对信息进行行空间预编码,从而构建不依赖于MIMO信道矩阵的期望信号子空间和自干扰子空间,实现未知信道状态下自干扰抵消和期望信号分离,从而消除非理想信道估计带来的剩余自干扰信号。在此基础上,以最大化有效信噪比为目标设计最佳预编码,通过推导可达和速率的闭合表达式,分析不同方案下信道估计误差对可达和速率的影响。仿真结果表明,新方案在不同的信道估计误差下,能够实现完美自干扰消除,其检测性能和容量均优于基于信道估计的自干扰消除方案。      

An efficient antenna shuffling scheme for a DSTTD system

A new antenna shuffling scheme for DSTTD (double space time transmit diversity) is proposed. The proposed method obtains the shuffling pattern directly from the estimated channel by maximizing minimum post-processing SNR (signal to noise ratio), while the conventional method minimizes channel correlation. Since the minimum post-processing SNR is directly related with error performance, the proposed method shows better bit error rate performance than the conventional method.     

一种适用多种空时编码方案的MIMO自编码器通信系统

为提升现有端到端通信系统的适应性与信道估计的准确性,提出了一种适用于多种空时编码方案的多输入多输出(Multiple-Input Multiple-Output,MIMO)自编码器通信系统.该系统将基于卷积神经网络的自编码器引入到MIMO系统中,并结合信道估计网络实现信道均衡,通过端到端的学习方式实现各种空时编码方案下...     

基于误差修正的MIMO信道半盲估计方法

近年来，由于多输入多输出（MIMO）通信系统有传输速率高等许多优点，受到人们的重视。在本文中，提出了一种MIMO信道估计的方法，利用了已知训练序列和全部的数据帧信息，通过估计值的误差对信道参数作进一步的调整，只需使用较短的训练序列即可避免陷入局部最小，从而提高了信息传输速率和信道估计的鲁棒性，提高了估计的精度，而且具有较低的计算复杂度。     

Low power ring oscillator for IoT applications

In order to scale with the demand of higher data rates and improved spectral efficiency in next generation wireless communication systems, a large-scale multiple-input and multiple-output (MIMO) technology called massive MIMO has been proposed. In massive MIMO, appropriate signal-to-noise ratio (SNR) values can be achieved by the addition of base station (BS) antennas in place of increasing transmit power. Pilot-based channel estimation is widely used in conventional MIMO systems, where pilot signal sequences are sent from the user terminals (UTs) to the BS to estimate the channel. In massive MIMO-based cellular networks, channel estimation in a given cell will be impaired by the pilot signal sequences transmitted by users in other cells—rendering the addition of antennas or transmit power ineffective. This effect is called pilot contamination. Therefore, pilot-based channel estimation limits the performance of massive MIMO. Semi-blind and blind methods are alternatives to pilot-based channel estimation that perform channel estimation with short pilot signal sequences and without pilot signal sequences, respectively. Blind channel estimation is one of the promising solutions to the pilot contamination problem in massive MIMO. This paper compares, using MATLAB simulations of a cluster-based COST 2100 channel model, the performance of pilot-based, semi-blind, blind, and adaptive-blind channel estimation methods. The pilot contamination effect on different channel estimation methods and how channel estimation methods can be used to overcome pilot contamination are shown. Finally, an adaptive independent component analysis (ICA)-based channel estimation method, which outperforms conventional ICA in terms of computational complexity, is proposed.     

基于快速张量分解的波束空间MIMO雷达二维DOA估计算法

传统MIMO雷达由于采用全向发射模式导致目标增益损失严重,致使DOA估计算法性能较差.因此,本文提出基于波束空间MIMO雷达的张量模型和快速张量分解的二维DOA估计算法.波束空间MIMO雷达能够通过发射波束成形技术将发射能量集中到指定空域,弥补传统MIMO雷达的发射增益损失.通过高阶张量模型应用MIMO雷达多脉冲接收数...     

Channel estimation and channel tracking for correlated block-fading channels in massive MIMO systems

This paper presents a channel estimation and tracking method for correlated block-fading channels in massive MIMO wireless cellular systems. In order to conserve resources, the proposed algorithm requires the uplink pilot signal only once, at the start of communication. By utilizing the temporal correlation between consecutive Resource Blocks (RBs) and the error correction capability of turbo codes, the channel matrix in subsequent RBs is estimated at the Base Station (BS) itself using the uplink data of current the RB and the estimated channel matrix of previous the RB. Compared to existing blind estimation methods, the proposed method places fewer limitations on the system settings such as the number of BS antennas, the number of users, and the number of coherent channel usage compared to existing blind estimation methods. Simulation results show that the proposed algorithm provides better performance for a moderate RB size, a high-order of QAM scheme, and a smaller ratio of the number of BS antennas and mobile terminals (N/K). For a reasonably small N/K (order of 10), the proposed scheme achieves a lower symbol error probability than the conventional pilot-based estimation approach.     

Computationally efficient DOD and DOA estimation for bistatic MIMO radar with propagator method

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Cramér–Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm.     

Blind-Channel Estimation for MIMO Systems With Structured Transmit Delay Scheme

This paper considers blind-channel estimation for multiple-input multiple-output (MIMO) systems with structured transmitter design. First, a structured transmit delay (STD) scheme is proposed for MIMO systems. Unlike existing transmit diversity approaches, in which different antennas transmit delayed, zero padded, or time-reversed versions of the same signal, in the proposed scheme, each antenna transmits an independent data stream, therefore promises higher data rate and more flexibility to transmitter design. Second, second-order statistics based blind-channel estimation algorithms are developed for MIMO systems with STD scheme. Channel identifiability is addressed for both correlation-based and subspace-based approaches. The proposed approaches involve no pre-equalization, have no limitations on channel zero locations, and do not rely on nonconstant modulus precoding. Third, when channel coding is employed, estimation accuracy can be further enhanced through “postprocessing”, in which channel estimation is refined by taking the tentative decisions from the channel decoder as pseudo-training symbols. Simulation examples are provided to demonstrate the robustness and effectiveness of the proposed approaches.      

Semiblind Channel Estimation for MIMO Single Carrier With Frequency-Domain Equalization Systems

We propose a semiblind channel-estimation method for multiple-input multiple-output (MIMO) single carrier with frequency-domain equalization systems. By taking advantage of periodic precoding and the block circulant channel model after cyclic prefix removal, we obtain the channel-product matrices by solving a series of decoupled linear systems, which is gained from the covariance matrix of the received data. Then, the channel-impulse-response matrix is obtained by computing the positive eigenvalues and eigenvectors of a Hermitian matrix formed from the channel-product matrices. We also propose an optimal design of the precoding sequence, which minimizes the noise effect and numerical error in covariance matrix estimation, and discuss the impact of the optimal sequence on channel equalization. With the proposed framework, the method is shown to be robust with respect to channel-order overestimation, and the identifiability condition is simply that the channel-impulse-response matrix has full column rank. Due to the identifiability condition, the method is applicable to MIMO channels with more transmitters or more receivers. Simulations are used to demonstrate the performance of the proposed method.      

MIMO Channels in the Low-SNR Regime: Communication Rate, Error Exponent, and Signal Peakiness

We consider multiple-input multiple-output (MIMO) fading channels and characterize the reliability function in the low signal-to-noise (SNR) regime as a function of the number of transmit and receive antennas. For the case when the fading matrix H has independent entries, we show that the number of transmit antennas plays a key role in reducing the peakiness in the input signal required to achieve the optimal error exponent for a given communication rate. Further, by considering a correlated channel model, we show that the maximum performance gain (in terms of the error exponent and communication rate) is achieved when the entries of the channel fading matrix are fully correlated. The results we presented in this work in the low-SNR regime can also be applied to the infinite bandwidth regime     

基于压缩感知的大规模MIMO信道估计与反馈

针对大规模 MIMO系统信道估计精度低及反馈方案较为复杂的问题,在差分信道估计及反馈方案上提出了一种基于系数相关性的压缩采样匹配追踪（BCC-CoSAMP）算法。该算法将CoSAMP算法中衡量两个向量之间关系的内积替换为基于相关系数的向量关系判定,从而较快地选出与原始信号相关性强的原子,达到提高信道估计精度的目的。仿真结果表明,与CoSAMP算法相比,所提出的BCC-CoSAMP算法在低信噪比情况下,信道估计精度平均有5 dB的提高,同时能平均提高系统总速率1.25 bit/(s.Hz)。     

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.     

基于叠加训练的非合作多用户/MIMO信道估计

提出一种基于叠加训练的单载波非合作多用户/MIMO系统的迭代信道估计与检测方案。首先利用变换域方法构造具有零周期互相关特性的训练序列,从而消除多天线间的相互干扰,实现基于一阶统计量的信道估计。然后采用联合符号检测的迭代信道估计方法,利用检测序列作为额外的“训练序列”来降低信息序列自身干扰。与现有的叠加训练信道估计方案比较,新方案中训练序列构造更加灵活,在低信噪比下信道估计均方误差和误码率性能更优,且复杂度更低,仿真结果表明了该方案的有效性。     

Parameter estimation and equalization techniques for communication channels with multipath and multiple frequency offsets

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame/spl acute/r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation.     

基于导频信号的MIMO-AF协作中继信道估计

提出一种在2个时隙内对MIMO-AF协作两跳中继信道进行估计的方法.重点研究在信源发送功率和中继发送功率受限的约束条件下,运用MMSE准则构建信源至中继节点信道估计的优化问题,采用矩阵分解的方法将信号分解成酉分量和对角分量,简化代价函数.最后用二分法求出最佳的导频信号和中继放大系数.仿真结果表明,本方法能够得到确定的、精度较高的信道估计值,并分析了信道相关性、天线数目对信道估计的影响.