基于整体最小二乘的联合信道估计及OFDM信号检测算法

针对现有的联合信道估计及OFDM信号检测算法性能不高的问题,该文提出一种基于整体最小二乘的联合信道估计及OFDM信号检测算法。该算法首先通过导频估计初始的信道信息,在此基础上不断地采用整体最小二乘进行OFDM信号检测及信道估计,有效缓解迭代模型误差的影响,加快了算法迭代的收敛速度,提高了信道估计的精度,从而降低了OFDM系统的误码率。该文推导的信道估计克拉美罗界及仿真结果均表明所提出的算法在时变信道环境下优于现有的联合信道估计及OFDM信号检测算法。     

基于平行因子分解的大规模MIMO盲信道估计

针对单小区大规模多输入多输出（Multiple-Input Multiple-Output,MIMO）系统上行链路,提出了一种基于平行因子（Parallel Factor,PARAFAC）模型的信道估计方法。在基站端,将接收信号构造成PARAFAC模型,利用大规模MIMO系统中信道的渐近正交的性质,提出了一种基于约束二线性迭代最小二乘算法(Constrained Blinear Alternating Least Squares,CBALS),从而实现了盲信道估计。理论分析及仿真结果表明,所提方法与传统最小二乘方法相比,不仅提高了频带利用率而且具有更高的估计精度;与已有的二线性交替最小二乘方法(BALS)相比,所提算法有更快的收敛速度。     

地空通信中基于贪婪算法的SC-FDE系统稀疏信道估计

针对在地空频率选择性衰落信道中,信道冲激响应具有时域稀疏特性的情况,提出了基于贪婪算法的单载波频域均衡(SC-FDE)系统稀疏信道估计方法。Chu序列是SC-FDE中常用的导频序列,对其进行分析并证明了将Chu序列进行循环移位所构造的导频矩阵满足RIP条件,将导频矩阵作为测量矩阵,把地空信道估计问题建模为稀疏重构模型,采用贪婪算法中的OMP和Co Sa MP算法对信道进行稀疏重构,仿真验证了所得信道估计较传统最小二乘(LS)信道估计方法更加准确。在相同的训练序列长度和信道环境下,利用所得信道估计对接收信号进行最小均方误差(MMSE)均衡,蒙特卡罗仿真结果表明,所提方法与传统LS信道估计方法相比,系统误码性能提高2~3 d B。     

基于ML的多径信道下直扩信号PN码和信道的联合盲估计

针对非协作通信下多径信道直接序列扩频信号伪随机(PN,Pseudorandom)码的估计难题,本文在分析信号二阶统计特性的基础上,提出了一种基于最大似然(ML,Maximum Likelihood)的PN码和多径信道联合盲估计的方法.该方法首先建立PN码和信道序列的最大似然数学模型,然后通过交替转换数学模型和使用迭代最小二乘投影(ILSP,Iterative Least Square Projection)算法实现PN码和多径信道的联合估计.为了进一步降低算法复杂度和避免矩阵求逆,本文给出了算法的自适应求解方式.此外,针对低信噪比下信道估计误差引起PN码的估计精度下降的问题,本文提出了一种基于迭代总体最小二乘投影的改进算法.所提算法不受PN码码型限制,并通过仿真实验验证了算法的有效性.     

小波包多载波系统中的LMS信道估计

为了提高正交小波包波分复用(OWPDM)系统抗多径衰落和多普勒频移的性能,提出了一种新的半盲信道估计算法。首先利用时域滤波原理滤去信道响应中的噪声,之后利用信号在时域补零相当于频域内插的原理来获得更精确的最小均方算法(LMS)估计器迭代初值。仿真结果表明,在插入更少导频数目的情况下,新算法估计性能优于最小二乘(LS)信道估计算法,在插入相同数目导频的情况下,与传统的LMS信道估计算法相比,新算法能使系统获得更好的误码性能。     

一种改进的MIMO-OFDM的信道估计算法

针对频率选择性衰落信道下MIMO—OFDM的系统,基于瑞利衰落信道的模型,利用m序列的自相关性,提出了一种基于m序列做梳状导频的多输入多输出正交频分复用最小二乘算法,该算法可以避免对大矩阵求伪逆,以减少计算复杂度,从而提高了信道估计的计算性能。通过对该算法的误码率性能分析和计算复杂度分析,结果表明,相比传统经典最小二乘算法,所提出信道估计算法在中低信噪比下,有效提高了信道估计性能,适合于在实际应用中实现。     

针对块稀疏信道的估计算法

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的最小二乘算法比较,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。      

双IRS辅助大规模MIMO系统中基于双时间尺度的信道估计方案

在双智能反射面（IRS）辅助的大规模多入多出（MIMO）通信系统中,为获取信道状态信息（CSI）而进行的信道估计需要较大的导频开销。考虑到基站（BS）-IRS信道与IRS-IRS信道具有高维且准静态的特性,而IRS-用户（UE）信道具有低维且时变的特性,本文提出一种基于双时间尺度的信道估计方案。首先,对于准静态信道,由BS发送导频并基于坐标下降算法和最小二乘法分别估计BS-IRS信道和IRS-IRS信道。然后根据准静态信道的估计结果进一步基于最小二乘法估计IRS-UE信道。仿真结果表明,本文所提出的方案可以采用低于对比方案50%的导频开销来获取低于对比方案近10 dB的估计归一化均方误差（NMSE）,实现用更少的导频开销获取更高的信道估计精度。     

基于IOC-CSMP的OFDM系统稀疏信道快速重构算法

针对信道路径数量未知时正交频分复用（OFDM）系统信道估计问题,提出了一种基于内积运算优化与稀疏度更新约束的压缩采样匹配追踪快速重构算法。通过构建与更新选择向量,利用与选择向量中非零值索引对应的原子向量参与内积运算来降低运算量;基于压缩采样与回溯策略来优化原子,利用匹配追踪完成信道估计,通过相邻两次信道估计值的能量差来更新稀疏度并约束算法停止,保证算法快速收敛。仿真结果表明,所提算法具有比最小二乘、最小均方差、稀疏度自适应匹配追踪和自适应正则化压缩采样匹配追踪算法更好的信道估计性能,且比2种自适应方法消耗更少的信道估计时间。     

OFDM稀疏信道估计中导频设计算法

由于无线信道固有的稀疏性,压缩感知理论已被应用于正交频分复用(OFDM)系统的信道估计中来提高频谱利用率。文中研究OFDM系统稀疏信道中确定性导频的设计问题,针对互相关最小准则的不足,提出了一种基于测量矩阵互相关和测量矩阵列相关平方和最小化的联合算法。仿真结果表明,该算法的归一化均方误差(MSE)和误码率(SER)性能均优于基于互相关最小准则的随机搜索导频,相比于最小二乘算法,稀疏信道估计使用了更少的导频获得了更好的估计效果,提高了频谱利用率。     

Constrained Tensor Modeling Approach to Blind Multiple-Antenna CDMA Schemes

In this paper, we consider an uplink multiple-antenna code-division multiple-access (CDMA) system linking several multiple-antenna mobile users to one multiple-antenna base station. For this system, a constrained third-order tensor decomposition is introduced for modeling the multiple-antenna transmitter as well as the received signal. The constrained structure of the proposed tensor decomposition is characterized by two constraint matrices that have a meaningful physical interpretation in our context. They can be viewed as canonical allocation matrices that define the allocation of users' data streams and spreading codes to the transmit antennas. The distinguishing features of the proposed tensor modeling with respect to the already existing tensor-based CDMA models are: i) it copes with multiple transmit antennas and spreading codes per user and ii) it models several spatial spreading/multiplexing schemes with multiple spreading codes by controlling the constrained structure of the tensor signal model. A systematic design procedure for the canonical allocation matrices leading to a unique blind symbol (or joint blind symbol-code) recovery is proposed which allows us to derive a finite set of multiple-antenna schemes for a fixed number of transmit antennas. Identifiability of the proposed tensor model is discussed, and a blind multiuser detection receiver based on the alternating least squares algorithm is considered for performance evaluation of several multiple-antenna CDMA schemes derived from the constrained modeling approach.     

基于嵌套张量模型的MIMO中继系统组合接收算法

目前在单向双跳多输入多输出(MIMO)中继系统中,基于嵌套张量模型的接收算法主要采用单步交替最小二乘(ALS)和KRF(Khatri-Rao Factorization)算法.在时变信道且实时性要求较高场景下,计算复杂度高是制约其应用的主要因素.为此,在对单向双跳MIMO中继系统建模基础上,提出了基于嵌套张量模型的双步组合接收算法.该算法通过对接收的数据张量进行重建,将符号估计和信道估计分离,充分利用ALS和KRF的算法优势,有效降低了计算复杂度.同时,对算法的可辨识性进行了分析.仿真结果表明,该算法保持了与传统嵌套PARAFAC的最小二乘(Nested PARAFAC ALS)算法的相同估计性能,在源天线个数变化时,计算复杂度降低了80%以上;在中继天线个数变化时,计算复杂度降低了50%以上.     

基于 GSVD 的分布式 MIMO 雷达测向算法

针对分布式多输入多输出（multi-input multi-output, MIMO）雷达测向中存在的数据信息提取不充分、运算量偏大等问题，开展了基于广义奇异值分解（generalized singular value decomposition, GSVD）的测向算法研究，以提高低信噪比条件下的角度估计性能。首先，建立了分布式阵列MIMO雷达回波信号的统一化表征模型；其次，将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题，运用交替最小二乘（alternating least squares, ALS）技术实现阵列信号流行矩阵的拟合，并引入子空间类算法实现目标角度联合估计；最后，对优化问题增加l1范数约束，避免了每次迭代中进行的奇异值分解运算，降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。     

Blind Joint Channel Estimation and Data Detection for Precoded Multi-Layered Space-Frequency MIMO Schemes

Due to the scarcity of the electromagnetic spectrum, multidimensional signaling schemes that take into account several signal dimensions such as space, time, frequency and constellation, are good candidates for increasing the data rate and/or improving the link reliability in future communication systems. This work addresses the problem of joint channel estimation and data detection in precoded multi-layered space-frequency codes (MLSFC) in multiple input multiple output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM). First, we consider a modified (precoded) MLSFC transmit structure that consists in extending the constellation rotation across multiple OFDM symbols. By recasting the received signal as trilinear model, we propose a low-complexity blind receiver based on the least squares Khatri–Rao factorization (LSKRF). Our proposed LSKRF receiver is a closed-form solution and it outperforms the classical alternating least squares (ALS) receiver while being less complex since no iteration is need. Our results attest to the benefits of the proposed receiver in a variety of MLSFC schemes in comparison with the non-blind zero-forcing-based MLSFC receiver that assumes perfect channel knowledge.     

基于多中继导频频分复用的协同通信系统信道估计算法

本文针对频率选择性衰落环境下结合正交频分复用技术的放大转发多中继协同通信系统,提出了基于导频频分复用的频域信道估计算法,包括最小二乘（LS）估计算法和线性最小均方误差（LMMSE）估计算法。理论分析和仿真结果表明,该算法不仅成功分辨了多中继协同通信系统的所有频域信道衰落系数,避免了各中继节点转发的导频符号在目的节点上的混叠干扰,而且减少了频域信道估计所需的导频符号数量和时隙周期长度,提高了协同通信系统的传输效率和频谱利用率,同时显著提高了信道估计的精度,降低了算法的复杂度,具有较高的实用价值。      

Joint detection and estimation for cooperative communications in cluster‐based networks

A joint data detection and channel estimation scheme is proposed for cooperative communications in cluster‐based networks. In the proposed approach, each relay detects the source information using its joint detection and estimation rules and then compresses this information and forwards it to the destination node. The source information received from all the relays in the network is combined at the destination node in accordance with a fusion decision rule in order to determine the corresponding symbol vector. The joint detection and estimation rule at the relays and the fusion rule at the destination node are both processed using an expectation maximization algorithm. In order to reliably avoid poor local maxima, we utilized one pilot symbol per signal block to obtain a better initial starting point for the expectation maximization algorithm. The simulation results show that the feedback mechanism accelerates the convergence of the local channel estimates toward the Cramer–Rao lower bound. Copyright © 2011 John Wiley & Sons, Ltd.     

Blind signal separation for coprime planar arrays: An improved coupled trilinear decomposition method

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.     

MC-CDMA系统迭代检测中的一种信道估计算法

在DS—CDMA系统中，联合译码的迭代多用户检测是克服多址干扰增加系统容量的有效方法。本文将多用户检测和译码相结合的迭代检测技术应用于MC—CDMA系统，其中，多用户检测器由串行干扰消除和其后的MMSE滤波器组成。文中提出一种时域信道估计作为迭代初始值的频域信道估计算法，比单纯的频域信道估计方法节约导频符号数量，并且由于信道估计性能的改善，加快了迭代检测的收敛速度。     

Channel estimation with ISFLA based pilot pattern optimization for MIMO OFDM system

In multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the channel state information should be known by the receiver for obtaining transmitted data. Channel estimation algorithms are used to examine the multipath effects of frequency selective Rayleigh fading channels. In this paper, Compressed Sensing (CS) based channel estimation technique is considered for reconstructing the signal with improved spectral efficiency. It requires transmitting the known pilot data to the receiver for estimating channel information. The optimum pilot patterns are selected through reducing the mutual coherence of measurement matrix. In order to maximize the accuracy of sparse channel estimation and to reduce the computational complexity, an optimization algorithm Improved Shuffled Frog Leaping (ISFL) is proposed. When compared with the traditional estimation methods like least squares (LS), and minimal mean square error (MMSE), 4.7% of spectral efficiency is increased with ISFLA based channel estimation. Implementation results show that, by using the proposed algorithm, the bit error rate (BER) and Mean Square Error (MER) performance of the system is increased with 1.5 dB and 2 dB respectively.