An embedded pilot power based channel estimation and low-complexity feedback equalization scheme for OTFS system

Orthogonal Time Frequency Space modulation (OTFS) has evolved as an astounding modulation technique for high-speed communication in a doubly dispersive channel. In any wireless communication system, channel estimation and equalization are essential at the receiver to recover the transmitted data. To accomplish this for the emerging OTFS based systems, a modified embedded pilot-based channel estimation technique and low complexity feedback equalization algorithm for integer Doppler shifts in the delay-Doppler domain are proposed in this paper. Our channel estimation scheme exploits embedded-pilot arrangement, and the symbol equalization relies on the Interference calculation and its mitigation iteratively. To achieve this we contemplate a prudent arrangement of symbols in the OTFS frame in such a way that the Guard symbols prevent the interference between data symbols and the pilot symbol at the receiver. Two distinct lumps of received data of the same OTFS frame will be engaged in channel estimation and data detection. An analytical expression of the theoretical Cramer Rao Lower Bound (CRLB) is derived and plotted for the proposed channel estimation scheme. The attained simulation results for Bit-Error-Rate (BER) under the proposed scheme show a significant error rate improvement over the Minimum Mean Squared Error (MMSE) equalization algorithm. Further, a lower computational complexity is also achieved in comparison with modified MMSE detection and MP detection algorithms.     

Target parameter estimation for OTFS radar using sparse signal processing

In this paper, we consider a monostatic radar receiver for a joint communication and radar (JCR) system that transmits orthogonal time frequency space (OTFS) frames for target detection and parameter estimation. The circular prolate pulse shape (CPPS) is employed over the OTFS signal as it has lower out-of-band (OoB) power radiation in comparison with the rectangular pulse shaped (RPS) OTFS. The PAPR of CPPS OTFS signal shows lowest value for larger frame duration and hence the signal can be considered to be a good candidate for JCR system. In the Delay-Doppler (DD) domain, the radar channel is sparse and therefore, we model the target detection problem as a sparse recovery problem to generate target profiles with higher peak-to-sidelobe ratio (PSLR). The target detection is carried out in the DD domain, the time–frequency (TF) domain, and in the time domain (TD). Sparse signal recovery algorithms like the orthogonal matching pursuit (OMP) algorithm, the subspace pursuit (SP) algorithm, and the sparse Bayesian learning (SBL) based algorithm are used in target parameter estimation. The performance of these algorithms are compared in terms of their computational complexity, the root mean squared error (RMSE) in the estimates of range and velocity and PSLR value in the target profiles. Simulation results validate that the proposed CPPS OTFS based radar system could detect the targets accurately in all the three domains and produce target profiles with almost zero side lobes.     

水声正交频分多址上行通信稀疏信道估计与导频优化

针对水声正交频分多址(OFDMA)上行通信中用户导频数量少、分布不均匀, 导致传统内插信道估计方法产生误码平层的问题, 提出一种稀疏信道估计与导频优化方法. 基于压缩感知(CS)理论估计稀疏信道冲激响应, 并依据CS理论中测量矩阵互相关最小化原理, 提出基于随机搜索的导频图案和导频功率联合优化算法. 仿真结果表明, 所提方法在不同多径扩展信道下的性能均优于基于线性内插的最小二乘估计、未经导频优化的CS信道估计以及单纯基于导频图案优化的CS信道估计. 水池实验分别验证了交织式和广义式子载波分配的水声OFDMA上行通信性能, 在接收信噪比高于10 dB时利用所提方法实现了两用户接入的可靠通信.     

A low complexity burst channel estimation algorithm for FDD massive MIMO systems

To fully attain array gains of massive multiple-input multiple-output (MIMO) and its energy and spectral efficiency, deriving channel state information (CSI) at the base station (BS) side is essential. However, CSI estimation of frequency-division duplex (FDD) based massive MIMO is a challenging task owning to the required pilots, which are proportional to the number of antennas at the BS side. Therefore, the pilot overhead should be inevitably mitigated in the process of downlink channel estimation of FDD technique. In this paper, we propose a novel compressed sensing (CS) algorithm which takes advantage of correlation between the received and transmitted signals to estimate the channel with high precision, and moreover, to reduce the computational complexity imposed on the BS side. The main idea behind the proposed algorithm is to sort the specific number of maximum correlations as a common support in each iteration of the algorithm. Simulation results indicate that the proposed algorithm is capable of estimating downlink channel better than the counterpart algorithms in terms of mean square error (MSE) and the computational complexity. Meanwhile, the complexity of the proposed method linearly grows up when the number of BS antennas increases.     

最优路径搜索压缩感知水声通信信道估计

将信号恢复中最优路径搜索的A*正交匹配追踪(A*Orthogonal Matching Pursuit,A*OMP)伪贪婪算法引入到水声通信信道估计中,可以有效改善正交匹配追踪(OMP)算法容易陷入局部最优的问题,并提出了一种改进型的A*OMP水声信道估计算法。改进了路径初始化方式,同时为了避免过多迭代引起的未知误差,将前后两次迭代残差之差作为停止准则。在正交频分复用(OFDM)通信体制下,对OMP、A*OMP和本文改进方法的估计误差和误比特率进行了仿真对比,随着信噪比的增加,改进方法未出现误差平台,且受导频间隔影响较小。仿真结果表明相对于OMP算法和传统A*OMP算法,在高信噪比下改进方法的估计误差分别降低约2和1个数量级,海试数据结果验证了改进方法的可行性,其误比特率分别平均降低42.0%和4.7%。      

Pilot design and doubly-selective channel estimation for faster-than-Nyquist signaling

Being capable of enhancing the spectral efficiency (SE), faster-than-Nyquist (FTN) signaling is a promising approach for wireless communication systems. This paper investigates the doubly-selective (i.e., time- and frequency-selective) channel estimation and data detection of FTN signaling. We consider the intersymbol interference (ISI) resulting from both the FTN signaling and the frequency-selective channel and adopt an efficient frame structure with reduced overhead. We propose a novel channel estimation technique of FTN signaling based on the least sum of squared errors (LSSE) approach to estimate the complex channel coefficients at the pilot locations within the frame. In particular, we find the optimal pilot sequence that minimizes the mean square error (MSE) of the channel estimation. To address the time-selective nature of the channel, we use a low-complexity linear interpolation to track the complex channel coefficients at the data symbols locations within the frame. To detect the data symbols of FTN signaling, we adopt a turbo equalization technique based on a linear soft-input soft-output (SISO) minimum mean square error (MMSE) equalizer. Simulation results show that the MSE of the proposed FTN signaling channel estimation employing the designed optimal pilot sequence is lower than its counterpart designed for conventional Nyquist transmission. The bit error rate (BER) of the FTN signaling employing the proposed optimal pilot sequence shows improvement compared to the FTN signaling employing the conventional Nyquist pilot sequence. Additionally, for the same SE, the proposed FTN signaling channel estimation employing the designed optimal pilot sequence shows better performance when compared to competing techniques from the literature.     

基于点和直线段对应的扩展正交迭代位姿估计算法

正交迭代算法是基于点特征,全局且快速收敛的位姿估计算法,是目前性能最优的实时位姿估计算法之一.对摄像机观测到的直线段提出了其不确定性描述的新方法:目标空间直线段误差,并把该误差融入到正交迭代算法的位姿计算过程中,形成扩展正交迭代算法.该算法可以同时利用点和直线段特征.算法先把点共面性方程表达为与正交迭代算法中点共线性方程一致的数学形式,再根据目标空间点共面性误差定义了目标空间直线段共面性误差.接着把目标空间点共线性误差和直线段共面性误差二者之和作为误差函数.最后推导出使该函数最小化的迭代求解过程.实验结果表明,本文算法是有效的,精确的.与正交迭代算法的计算结果对比,由于可以同时利用直线段特征,扩展正交迭代算法的位姿估计误差更低,抗噪声性能更强,算法更稳定.     

基于双鸟群优化的高光谱图像非线性解混

针对高光谱图像像元中端元物质非线性混合的特点,借鉴生物群智能现象,提出一种基于双鸟群优化的高光谱图像非线性解混算法。为进一步提高非线性解混算法的精度,通过模拟鸟群中觅食、警惕以及飞行等行为得到非线性问题的最优解。算法通过双鸟群的迭代优化来交替更新目标函数中的最优解以及非线性模型参数,最终得到高光谱图像端元丰度的最佳估计。仿真实验和光谱数据实验结果表明:双鸟群优化算法迭代收敛,能克服局部最小值问题;相比于同类算法,该算法解混结果的丰度重建误差、平均光谱角距离和像元重建误差3项指标均较小,该算法解混精度高,像元重构效果好,能有效提高高光谱图像非线性解混的精度。     

Improving spectral efficiency via pilot assignment and subarray selection under realistic XL-MIMO channels

The main requirements for 5G and beyond connectivity include a uniform high quality of service, which can be attained in crowded scenarios by extra-large MIMO (XL-MIMO) systems. Another requirement is to support an increasing number of connected users in (over)crowded machine-type communication (mMTC). In such scenarios, pilot assignment (PA) becomes paramount to reduce pilot contamination and consequently improve spectral efficiency (SE). We propose a novel quasi-optimal low-complexity iterative pilot assignment strategy for XL-MIMO systems, based on a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality of service more uniform, taking into account the normalized mean-square error (NMSE) of channel estimation from each candidate of the population. The simulations reveal that the proposed iterative procedure minimizes the channel estimation NMSE averaged over the UEs. The second procedure is the subarray (SA) selection. In XL-MIMO systems, commonly a UE is close to an SA antenna subset such that a sufficient data rate can be achieved if only a specific SA serves that UE. Thus, an SA selection procedure is investigated to make the system scalable by defining the maximum number of UEs each SA can help. Hence, the SA clusters are formatted based on the PA decision. Furthermore, we introduce an appropriate channel model for XL-MIMO, which considers a deterministic LoS component with a distance-dependent probability of existence combined with a stochastic spatially correlated Rayleigh NLoS fading component. The developed simulations and analyses rely on this suitable channel model under realistic assumptions of pilot contamination and correlated channels.     

基于压缩感知理论的大规模MIMO系统下行信道估计中的导频优化理论分析与算法设计

针对大规模多输入多输出(multiple input multiple output, MIMO)系统信道估计中的导频设计问题,在压缩感知理论框架下,提出了一种基于信道重构错误率最小化的自适应自相关矩阵缩减参数导频优化算法.首先以信道重构错误率最小化为目标,推导了正交匹配追踪(orthogonal matching pursuit, OMP)算法下信道重构错误率与导频矩阵列相关性之间的关系,并得出优化导频矩阵的两点准则,即导频矩阵列相关性期望和方差最小化;然后研究了优化导频矩阵的方法,并提出相应的自适应自相关矩阵缩减参数导频矩阵优化算法,即在每次迭代过程中,以待优化矩阵平均列相关程度是否减小作为判断条件,调整自相关矩阵缩减参数值,使参数不断趋近于理论最优.仿真结果表明,与采用Gaussian矩阵、Elad方法、低幂平均列相关方法得到的导频矩阵相比,本文所提方法具有更好的列相关性,且具有更低的信道重构错误率.     

基于自适应量化的超光谱图像压缩

提出了一种基于谱间预测和小波量化编码的超光谱图像压缩方法。在充分考虑子带图像非平稳特性的基础上,首先实现了子带分类步骤,通过分类设计自适应预测器,提高了谱间去相关效率。由于子带中不同子类表现出了不同的统计特性,所以使用空变均匀阈值量化器完成了量化工作。研究了均匀量化器对不同分布训练样本的率 失真表现,并为相关特性建立了模型描述。基于率失真模型和系数序列的统计特性,提出了一个率分配算法,以便能为不同的子类系数序列设计率失真意义上的最优量化器。实验证明,这种方法能高效地压缩超光谱图像,表现出了优异的压缩性能。     

Iterative optimization of quantum error correcting codes

We introduce a convergent iterative algorithm for finding the optimal coding and decoding operations for an arbitrary noisy quantum channel. This algorithm does not require any error syndrome to be corrected completely, and hence also finds codes outside the usual Knill-Laflamme definition of error correcting codes. The iteration is shown to improve the figure of merit "channel fidelity" in every step.     

基于混合粒子滤波的水下小目标跟踪

针对水下小目标粒子滤波估计过程中“粒子贫化”引起的估计性能下降,提出了混合粒子滤波算法。该算法在常规粒子滤波算法基础上,在每一步迭代估计过程中进行量测的再次随机采样,以丰富随机粒子多样性,缓解水下小目标状态估计过程中的“粒子贫化”的影响。对算法进行了仿真分析,并将该方法用于水下小目标探测实验的数据处理,结果表明,相比于常规的粒子滤波算法,所提出的混合粒子滤波得到了误差更小且稳定的状态估计结果,有效地改善水下小目标跟踪的精度和稳健性。     

Bandwidth-efficient frequency-domain equalization for single carrier multiple-input multiple-output underwater acoustic communications

This paper proposes a single carrier (SC) receiver scheme with bandwidth-efficient frequency-domain equalization (FDE) for underwater acoustic (UWA) communications employing multiple transducers and multiple hydrophones. Different from the FDE methods that perform FDE on a whole data block, the proposed algorithm implements an overlapped-window FDE by partitioning a large block into small subblocks. A decision-directed channel estimation scheme is incorporated with the overlapped-window FDE to track channel variations and improve the error performance. The proposed algorithm significantly increases the length of each block and keeps the same number of training symbols per block, hence achieving better data efficiency without performance degradation. The proposed scheme is tested by the undersea data collected in the Rescheduled Acoustic Communications Experiment (RACE) in March 2008. Without coding, the 2-by-12 MIMO overlapped-window FDE reduces the average bit error rate (BER) over traditional SC-FDE schemes by 74.4% and 84.6% for the 400 m and 1000 m range systems, respectively, at the same data efficiency. If the same BER performance is required, the proposed algorithm has only 8.4% transmission overhead, comparing to over 20% overhead in other existing UWA OFDM and SC-FDE systems. The improved data efficiency and/or error performance of the proposed FDE scheme is achieved by slightly increased computational complexity over traditional SC-FDE schemes.     

基于发射率偏差约束的多光谱真温反演算法

基于二次测量的多光谱辐射测温反演算法由于无需事先假设发射率模型而受到广泛关注,但需要较长的迭代时间,并且需要设定合适初始温度和发射率范围。为此提出了基于发射率偏差约束的多光谱真温反演算法。将二次测量法中发射率连续迭代转变为发射率偏差约束后迭代,拟合了光谱发射率偏差和温度偏差之间的函数关系,依据此函数关系确定每次迭代所产生的发射率偏差,从而迅速减小发射率搜索范围,提高计算效率。针对四种光谱发射率模型的仿真结果表明,与二次测量法相比,新算法无需设定温度初值范围,在保证反演精度的前提下,运算效率提高60%以上。     

Near optimal performance joint semi-blind channel estimation and data detection techniques for Alamouti coded single-carrier (SC) MIMO communication systems

In this paper, two novel joint semi-blind channel estimation and data detection techniques are proposed and investigated for Alamouti coded single-carrier (SC) multiple-input multiple-output (MIMO) communication system using Rayleigh flat fading channel model. In the first novel semi-blind technique, blind channel estimation can be performed by using singular value decomposition (SVD) of received output autocorrelation matrix and training based channel estimation for orthogonal training symbols can be performed by using orthogonal pilot maximum likelihood (OPML) algorithm. Further using, that semi-blind channel estimate and received output, data detection is performed by using Maximum likelihood (ML) detection. Finally we derived new training symbols from error covariance matrix of estimated data and known orthogonal training symbols, which further applied to OPML algorithm for final channel estimate. In the second novel semi-blind technique, blind channel estimation can be performed by using matrix triangularization based on householder QR decomposition (H-QRD) of received output autocorrelation matrix instead of SVD decomposition. Other steps are same as the first novel technique to calculate data detection and final channel estimation. Simulation results are presented under 2-PSK, 4-PSK, 8-PSK and 16-QAM data modulation schemes using 2 transmitters and different combinations of receiver antennas to investigate the performances of novel techniques compare to conventional whitening rotation (WR) and rotation optimization maximum likelihood (ROML) based semi-blind channel estimation techniques. Result demonstrates that novel techniques outperform others by achieving near optimal performance.     

水声正交频分复用异步多用户接入方法

水声信道传播时延大导致水声通信网中上行用户同步困难,因此需要接收端具备异步多用户检测能力。针对水声正交频分复用(OFDM)异步多用户系统提出一种基于滑动迭代的异步多用户接入方法,包括重叠截断、干扰消除、频域过采样、多用户信道估计与解码、以及干扰重构5个处理模块,并通过滑动迭代结构同时消除前向干扰和后向干扰影响。仿真结果表明,所提方法在不同异步到达时延,以及有、无信道估计误差下的误比特率均明显低于逐块解调异步接收算法。水池实验结果表明,滑动迭代异步接收算法在多径扩展18 ms、异步到达时延大于或等于循环前缀1.5倍时实现了两用户异步接入的可靠通信。      

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

In massive multiple-input multiple-output (MIMO), it is much challenging to obtain accurate channel state information (CSI) after radio frequency (RF) chain reduction due to the high dimensions. With the fast development of machine learning(ML), it is widely acknowledged that ML is an effective method to deal with channel models which are typically unknown and hard to approximate. In this paper, we use the low complexity vector approximate messaging passing (VAMP) algorithm for channel estimation, combined with a deep learning framework for soft threshold shrinkage function training. Furthermore, in order to improve the estimation accuracy of the algorithm for massive MIMO channels, an optimized threshold function is proposed. This function is based on Gaussian mixture (GM) distribution modeling, and the expectation maximum Algorithm (EM Algorithm) is used to recover the channel information in beamspace. This contraction function and deep neural network are improved on the vector approximate messaging algorithm to form a high-precision channel estimation algorithm. Simulation results validate the effectiveness of the proposed network.     

变步长似p范数约束稀疏水声信道估计方法

针对范数约束类归一化最小均方(NLMS)算法在正交频分复用(OFDM)稀疏水声信道估计中误码率较高的问题,提出一种改进的变步长似p范数约束信道估计方法。采用改进双Logistic函数构造步长,并将误差信号自相关函数引入其中,实时调整步长和零吸引项,使得收敛速度和估计精度能够很好地折中。算法仿真结果表明,在浅海多径稀疏水声信道下,相比于传统方法,所提出的信道估计获得的最高性能提升为收敛速度提高72.3%,稳态误差降低95.9%。湖上试验数据处理结果显示,相比于传统信道估计方法,所提出的方法能使通信误码率降低2~3个数量级,实现零误码水声通信。      

基于OB-HMAD算法和光谱特征的高分辨率遥感影像变化检测

高空间分辨率遥感影像蕴涵丰富的地物细节信息,针对高分辨率多时相遥感影像的变化检测可以更清楚认识到地理单元的变化情况,传统的遥感变化检测算法面对高分辨率遥感影像时,会出现明显的"椒盐现象"。本文借鉴面向对象图像分析的思想,以高分辨率遥感影像对象的光谱特征为分析对象,在多变量变化检测算法(multivariate alternative detection, MAD)的基础上,提出一种半自动阈值选取的OB-HMAD(object based-hybrid MAD)算法,并利用该算法进行变化检测实验对比分析。首先对高分辨率多时相遥感影像进行多尺度分割,形成多通道的影像对象;其次利用MAD变换,形成差异影像对象,并对其进行MNF变换,提高影像对象的信噪比;然后采用直方图曲率分析(histogram curvature analysis, HCA)进行半自动阈值选取,提取变化区域;最后结合实地样本数据对变化检测结果进行混淆矩阵的精度验证。结合2012年和2013年北京地区Worldview-2影像的实验可知,OB-HMAD算法融合多通道的光谱信息,可以有效的实现多时相高分影像的变化检测,基本消除了基于像元变化检测中"椒盐"现象的干扰,并在一定程度上降低建筑物阴影和几何配准误差的影响,总体精度和kappa系数也较优于其他变化检测算法,但存在较大的漏检误差。MNF变换可以有效的提高影像的信噪比,使差异信息更集中,直方图曲率分析的阈值分割算法相对其他阈值算法,自动化程度更高。