Achievable rate of MIMO channels with data-aided channel estimationand perfect interleaving

The achievable rate of a coherent coded modulation digital communication system with data-aided channel estimation and a discrete equiprobable symbol alphabet is derived under the assumption that the system operates on a flat fading multiple-input/multiple-output channel and uses a perfect interleaver to combat the bursty nature of the channel. It is shown that linear minimum mean square error channel estimation directly follows from the derivation and links average mutual information to the channel dynamics. Based on the assumption that known training symbols are transmitted, the achievable rate of the system is optimized with respect to the amount of training information needed     

面向RIS-mmWave系统的机器学习辅助高效波束训练及信道估计方法

面向B5G及6G无线通信系统的高速无线信息传输,本文研究了智能超表面辅助毫米波（RIS-mmWave）系统的高效能波束训练及信道估计方法。特别地,基于RIS-mmWave波束管理及有效信道获取的内生关联性,本文创新性地提出一种机器学习辅助的RIS-mmWave系统高效波束训练及信道估计方法。具体而言,在第一阶段,设计了一种新颖的半监督学习模型,实现位置信息辅助的在线快速波束训练,并且免估计地直接获取粗略角度域信息以驱动精细化信道估计;在第二阶段中,提出半监督学习辅助的压缩感知级联信道估计算法,利用半监督学习模型直接输出的粗略角度域信息驱动块正交匹配追踪算法进行信道估计。仿真结果表明,所提波束训练及信道估计方法在系统开销和信道估计误差等方面的性能均优于代表性参考方案。     

基于宽带突发单载波频域均衡传输的时域精细信道估计方法

单载波频域均衡(SC-FDE)是宽带无线通信系统中一种具有竞争力的解决方案,获得了广泛的关注和研究。SC-FDE方案不仅抗多径能力出色、复杂度低,且具有比正交频分复用(OFDM)信号更低的峰均比值。在突发SC- FDE系统中,接收机需要利用训练序列快速获取信道的特征信息,以避免解调性能损失。传统的基于训练序列的信道估计方法并不适合宽带猝发SC-FDE系统,难以兼顾估计准确性和复杂度。该文提出一种基于时域训练序列的精细信道估计方法。该方法利用时域PN序列得到信道参数的最大似然估计值,并利用信道稀疏的特征,根据信道噪声强度对信道估计值进行噪声抑制处理。仿真表明:与传统信道估计方法相比,该信道估计方法能取得更高的估计精度,且具有较低的实现复杂度。     

Multi-antenna scheme for high capacity transmission in the digital audio broadcasting

In this paper, we apply multi-antenna scheme to DAB system for high-rate transmission. In the MIMO-DAB system with a number of transmitting and receiving antennas increases the transmission rate efficiently with low multiplication operations. For channel estimation in the MIMO-DAB system, we propose a new PRS structure and evaluate the channel estimation performance. The proposed PRS structure can estimate multi-channel information based on the semi-blind processing. The performance of proposed PRS is compared with that of conventional training sequence employing Tarokh's transmission matrix. And also we investigate the performance limitation due to the imperfect channel estimation in a MIMO-DAB receiver. It has been shown that the MIMO-DAB system with multi-antennas can achieve the high-rate transmission for multimedia broadcasting and the performance of MIMO-DAB system is impaired by imperfect channel information.     

隐含训练序列信道估计中的功率分配

与传统时分发送训练序列的信道估计算法相比,隐含训练序列信道估计算法将训练序列与信息序列直接相加后通过天线发送,从而节约了信道带宽。然而,在天线发送总功率一定时,训练序列的功率越大,信息序列的功率便越小,从而导致信道均衡器的信噪比减小。本文研究了基于MIMO系统的隐含训练序列信道估计算法,分析了信道均衡器信噪比与训练序列功率的关系,并根据均衡器信噪比最大原则推导出训练序列与信息序列的最佳功率分配。分析和仿真结果表明:在训练序列的最佳功率点上,信道均衡器的信噪比最高;随着接收天线信噪比的增加,训练序列的最佳功率增大。     

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。     

基于Turbo原理的迭代UWB信道估计方法

针对UWB系统室内密集离散多径信道,提出一种基于Turbo原理的无训练比特的迭代信道估计方法和接收机结构.在迭代信道估计过程中,译码器输出的软信息反馈给信道估计模块,作为信道估计的先验信息使用.以有效提高信道利用率和接收机的性能.仿真结果表明,该信道估计方法能够有效估计信道参数,并且对于信道变化具有较强的跟踪估计能力.     

Power allocation in vector estimation systems with the impact of wireless channel uncertainty

Motivated by the fact that wireless channel uncertainty always exists and influences the distributed estimation system, this paper proposes power allocation schemes for linear minimum mean square error estimation. We consider training‐based vector systems and investigate how the power allocation ratio between training and transmitting is influenced by the system information, assuming that the sum of training and data transmitting power is fixed. We propose to use the average mean square error as the distortion measure so as to fulfill the statistical characteristics of channel estimation. We derive the closed‐form solutions to the optimal power allocation ratio, which is the function of system parameters, such as the vector signal's size, channel covariance, and noise covariance. Monte Carlo simulations are carried out to verify the performance of the proposed methods. Simulation results show that (i) in vector estimation system, the power allocation set to be 0.5 is usually not optimal; (ii) compared with training based, the equal power allocation system, the newly proposed methods could significantly improve the estimation performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Joint channel estimation and resource allocation for MIMO systems-part I: single-user analysis

Multiple antenna systems are known to provide very large data rates, when the perfect channel state information (CSI) is available at the receiver. However, this requires the receiver to perform a noise-free, multi-dimensional channel estimation, without using communication resources. In practice, any channel estimation is noisy and uses system resources. We shall examine the trade-off between improving channel estimation and increasing the achievable data rate. We consider transmitside correlated multi-input multi-output (MIMO) channels with block fading, where each block is divided into training and data transmission phases. The receiver has a noisy CSI that it obtains through a channel estimation process, while the transmitter has partial CSI in the form of covariance feedback. In Part I of this two-part paper, we consider the single-user case, and optimize the achievable rate jointly over parameters associated with the training phase and data transmission phase. In particular, we first choose the training signal to minimize the channel estimation error, and then, develop an iterative algorithm to solve for the optimum system resources such as time, power and spatial dimensions. Specifically, the algorithm finds the optimum training duration, the optimum allocation of power between training and data transmission phases, the optimum allocation of power over the antennas during the data transmission phase.     

On the performance of data-dependent superimposed training without Cyclic Prefix for SISO/MIMO systems

Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for th...     

基于DDST频域能量最大化的频偏估计方法

数据依赖叠加训练序列(Data-Dependent Superimposed Training,DDST)常用在正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)的信道估计中,由叠加训练序列和数据依赖序列组成,与信息序列并行发送,可以提高带宽利用率。提出了一种基于数据依赖叠加训练序列的OFDM载波频偏估计方法。叠加训练序列的周期性使其DFT能量间隔分布在特定的频点上,利用这个特性可进行频偏估计,只有得到正确的频偏估计时,这些特定频点的能量才得到最大值。仿真表明该方法在不降低传输速率的情况下,有着较好的频偏估计性能。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计。基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性。     

Robust receiver design for MIMO single-carrier block transmission over time-varying dispersive channels against imperfect channel knowledge

We consider MIMO single-carrier block transmission over time-varying multipath channels, under the assumption that the channel parameters are not exactly known but are estimated via the least-squares training technique. While the channel temporal variation is known to negate the tone-by-tone frequency-domain equalization facility, it is otherwise shown that in the time domain the signal signatures can be arranged into groups of orthogonal components, leading to a very natural yet efficient group-by-group symbol recovery scheme. To realize this figure of merit we propose a constrained-optimization based receiver which also takes into account the mitigation of channel mismatch effects caused by time variation and imperfect estimation. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe-canceller setup. This enables us to directly model the channel mismatch effect into the system equations through the perturbation technique and, in turn, to further exploit the statistical assumptions on channel temporal variation and estimation errors for deriving a closedform solution. Within the considered framework the proposed robust equalizer can be combined with the successive interference cancellation mechanism for further performance enhancement. Flop count evaluation and numerical simulation are used to evidence the advantages of the proposed scheme.     

突发通信中基于叠加训练的信道估计方案

针对无线突发通信中频带利用率低、信道参数获取困难等问题,提出了一种应用于突发通信的叠加训练信道估计与检测方案。该方案将信息和训练叠加发送,通过预失真发送信息符号使得训练与信息在频域正交,收端采用一阶统计信道估计和最大似然符号检测,并设计了抗直流干扰的信道估计方案。仿真表明,新方案在消除训练序列的频带开销的情况下获得了较好的信道估计和符号检测性能,与采用时分复用训练的方案相比,其有效吞吐率更优。     

Blind Channel Estimation for OFDM Systems via a Generalized Precoding

In this paper, we consider the problem of blind channel estimation for single-input-single-output (SISO) orthogonal frequency-division multiplexing (OFDM) system via second-order statistics only. Based on the assumption that the transmitted symbols are independent and identically distributed, we develop a simple blind channel estimation technique for OFDM systems by utilizing a generalized linear nonredundant block precoding. Instead of using partial information from the signal covariance matrix, as done in previous works where a specific precoder is designed and only one column of the signal covariance matrix is exploited, our work jointly considers all the information contained in the signal covariance matrix. Compared to the popular subspace-based blind channel estimation methods, the proposed algorithm is much more computationally efficient. A design criterion of the precoders by which the performance can be improved is provided, and the closed-form stochastic Crameacuter-Rao bound is derived. The numerical results clearly show the effectiveness of our proposed algorithm, as well as its improvement over the existing techniques     

Joint Synchronization and Channel Estimation for OFDM Systems

1Introduction Orthogonal Frequency Division Multiplexing(OFDM) has become a popular technique for transmis-sion of signals over wireless channels . OFDMhas beenadoptedinseveral wireless standards such as Digital Au-dio Broadcasting ( DAB) , Digital Video Broadcasting(DVB-T) ,the IEEE 802 .11a[1]Local Area Network(LAN) standard and high performance LAN type 2(HIPERLAN/2)[2]standard. OFDMis also being pur-sued for Dedicated Short-Range Communications(DSRC) for road side to …     

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.     

Multiple-Antenna Signaling Over Fading Channels With Estimated Channel State Information: Capacity Analysis

Multiple-antenna concepts for wireless communication systems promise high spectral efficiencies and improved error rate performance by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the capacity of Rayleigh and Ricean block-fading channels. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. The training symbols are used to estimate the channel state information (CSI) at the receiver by means of an arbitrary linear estimation filter. No CSI is assumed at the transmitter. Our analysis is based on an equivalent system model for training-based multiple-antenna systems which specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent, multiplicative noise. This model includes the special cases of perfect CSI and no CSI. We present new upper and lower bounds on the maximum instantaneous mutual information to compute ergodic and outage capacities, and extend previous results to arbitrary (and possibly mismatched) linear channel estimators and to correlated Ricean fading. Several numerical results for single- and multiple-antenna systems with estimated CSI are included as illustration.     

Doubly-Selective Channel Estimation Using Data-Dependent Superimposed Training and Exponential Basis Models

Channel estimation for single-user frequency- selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be well- approximated by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor signal-to-noise ratio (SNR). In this paper a data-dependent superimposed training sequence is used to cancel out the effects of the unknown information sequence at the receiver on channel estimation. A performance analysis is presented. We also consider the issue of superimposed training power allocation. Several illustrative computer simulation examples are presented.     

基于部分数据的叠加序列慢时变信道估计

针对OFDMA通信系统,提出了一种基于部分数据的叠加序列慢时变信道估计算法,并在接收端给出了数据恢复的方法。时变信道采用复指数基扩展模型来描述,对OFDMA系统的导频序列进行了精心设计。提出在频域减去一个基于部分数据的序列,从而使发送数据经过反傅里叶变换后,特定位上的数据能量变低,进而大大减少了数据在信道估计时产生的误差。由于在发射端信号已经产生了畸变,在接收端采用特定的方法对数据进行补偿,消除了这种影响。实验仿真证明该方法在一定程度上提高信道估计的性能。