MIMO-OFDM系统中的信道估计算法

本文对MIMO—OFDM系统中的信道估计技术进行了介绍,根据是否使用训练序列,信道估计可以分为导频辅助信道估计、盲信道估计及半盲信道估计,本文分别对已有的信道估计算法进行了综述,并对一种已有的基于m序列的时域信道估计方法进行了改进。     

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。     

利用数据和导频的多小区Massive MIMO系统信道估计

本文提出一种通过同时利用上行数据和导频进行信道估计来对抗时分双工大规模多输入多输出（Massive multi-input multi-output,Massive MIMO）系统中导频污染的方法。考虑到Massive MIMO系统下空间相关信道在角度域具有近似稀疏性,期望用户信道和干扰信道的多径分量因而可分辨。同时上行数据子帧中的数据符号数远大于导频序列的长度。基于以上两点观察,首先利用上行导频对信道进行粗估计,然后将解码得到的上行数据符号视为导频序列,用于得到角度域信道估计中能量较大分量所处的位置。通过抽取信道粗估计中相应位置上的信道系数,可以得到信道的精估计。仿真表明,无论相邻小区采用同一组正交导频序列、还是不同组但具有相关性的导频序列,所提出的信道估计方法都能有效地消除导频污染。      

平坦信道下的MIMO信道估计误差分析

基于导频序列推导了频率平坦多输入多输出(MIMO)信道下的最小二乘信道估计(LS)误差;将信道估计误差等效为高斯噪声,推导了信道估计条件下系统的等效信噪比,提出了用系统的等效信噪比来分析和评估在信道估计误差条件下系统的误码性能;最后用等效信噪比的方法评估了ZF-V-BLAST算法在信道估计下的性能,结果表明和计算机仿真结果基本相吻合。     

OFDM中基于导频的加窗FFT信道估计

正交频分复用（OFDM）是一种正交的多载波复用技术,分析了OFDM系统中基于导频的信道估计模型;基于导频的信道估计需要进行插值,为了降低插值算法的复杂度,研究了基于导频的FFT信道估计算法。由于直接FFT信道估计算法存在能量频谱泄露问题,提出了基于汉明窗函数的优化型加窗FFT信道估计的算法。实验仿真表明：在多径衰落信道下,该算法能够有效地提高OFDM系统的信道估计性能。     

MIMO-OFDM系统中一种链式加权LS信道估计算法

该文对多输入多输出正交频分复用系统中的信道估计进行研究,提出了一种链式加权LS信道估计算法。该算法基于频域正交循环导频序列,首先采用LS算法获得信道响应初始估值,然后对不同时刻的信道初值进行链式加权改善信道估值。由于循环导频结构保证了任何发射天线在训练周期内所有载波位置都有导频符号可供参考,该算法能够有效地抑制载波边界效应,提高信道估计的精度;同时它不需要信道先验信息以及矩阵求逆运算,因而实现简单。     

多径信道下MIMO系统发送天线数估计

为了解决认知无线电或信号截获中多径信道下MIMO系统发送天线数估计问题,首先分析了现有模型在多径信道下失效的原因,将MIMO多径信道模型等效变换出一种虚拟信道矩阵,从而建立多径信道下MIMO系统发送天线数估计模型;然后,利用随机矩阵理论中协方差矩阵最小特征值分布的相关研究结果,证明了时不变瑞利信道的协方差矩阵最小特征值收敛于第二类Tracy-Widom分布,分析了该特点对发送天线数估计的影响,并提出一种改进的RMT估计算法来估计多径信道下MIMO系统发送天线数.最后对改进算法进行了仿真验证,结果表明在低信噪比和小数据条件下,改进算法的估计性能相比RMT算法有较大提升.     

一种FDD大规模MIMO系统下行信道估计算法

信道估计是大规模多输入多输出(MIMO)系统的关键技术之一。本文针对频分双工(FDD)大规模MIMO正交频分复用(OFDM)系统,研究了下行信道估计问题。通过利用大规模MIMO-OFDM信道在角度-频域中的块稀疏特性,提出了基于块匹配追踪的低复杂度估计算法。另外,针对采用时域正交导频存在估计周期过长,有可能超过系统相干时间的问题,提出了天线分组发送方案,通过牺牲观测数据长度来换取信道估计周期的减少。仿真结果表明,所提算法具有良好的抗噪性能,可以准确找出稀疏向量的非零值位置,并可自适应确定稀疏度。     

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。     

采用数据辅助的大规模MIMO信道半盲迭代估计

由于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)信道衰落参数的维度较高,导致最优估计算法计算量大且需要的导频数较多而影响到频谱效率.为降低计算复杂度并减少导频开销,提出了两种基于期望最大化(Expectation Maximization,EM)估计的半盲迭代改进算法.利...     

Algorithms and architecture implementations of MIMO OFDM baseband receiver based on the SIMD DSP core

This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three coprocessors that are used for synchronization, FFT and channel decoder. In this MIMO OFDM system, the Zero Correlation Zone （ZCZ） code is used as the synchronization word preamble of packet in the physical layer in order to avoid the interference from other transmitting antennas. Furthermore, a simple channel estimation algorithm is proposed which is appropriate tbr the SIMD DSP computation.     

分布式MIMO-OFDM系统的同步捕获算法

该文提出了一种分布式多输入多输出正交频分复用(MIMO-OFDM)系统初始信号检测和同步捕获算法。该算法设计了导频符号,采用谱分析方法实现可靠的初始信号检测、粗定时和粗频偏估计,并在快速傅里叶变换(FFT)之后进行精确地频偏和定时估计。仿真结果表明,该算法在低信噪比多径瑞利信道条件下具有很好的性能。     

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station (BS) is equipped with a very large number of antennas (also referred to as “massive multiple-input multiple-output (MIMO)”). We consider a time-division duplexing (TDD) scheme, in which reciprocity between the uplink and downlink channels can be assumed. Channel estimation is essential for downlink beamforming in massive MIMO, nevertheless, the pilot contamination effect hinders accurate channel estimation, which leads to overall performance degradation. Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals (AOAs) in the large-scale antenna system, we propose a multiple signals classification (MUSIC) based channel estimation algorithm during the uplink transmission. Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.     

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.     

大规模MIMO系统上行链路时间-空间结构信道估计算法

针对大规模多入多出(MIMO)系统上行链路非平稳空间相关信道的估计问题,该文利用信道的时间-空间2维稀疏结构信息,应用狄利克雷过程(DP)和变分贝叶斯推理(VBI),设计了一种低导频开销和计算复杂度的信道估计迭代算法,提高了信道估计精度。由于平稳空间相关信道难以适用于大规模MIMO系统,该文借助于狄利克雷过程构建了非平稳空间相关信道先验模型,可将具有空间关联的多个物理信道映射为具有相同时延结构的概率信道,并应用变分贝叶斯推理设计了低导频开销和计算复杂度的信道估计迭代算法。实验结果验证了所提算法的有效性,且具有对系统关键参数鲁棒性的优点。     

时间相关块贝叶斯算法下的大规模MIMO信道估计

因具有高的阵列增益和高的频谱效率,大规模MIMO已成为5G通信系统物理层关键技术,但在频分双工系统基站侧获取大规模MIMO信道准确状态信息的过程中,存在导频开销占用大量频谱资源问题。为此,针对时间相关信道和信道稀疏度未知的情况,提出一种基于时间相关和多测量矢量模型的块贝叶斯压缩感知（TMBB-CS）信道估计方法。因基站端天线发射信号时间相关,所以大规模MIMO系统的时域信道脉冲响应呈块稀疏结构,利用该特性对下行链路中的多用户信道矩阵进行测量估计,可较大幅度减少导频开销,提升性能。实验仿真结果表明,与其他块贝叶斯算法相比,所提出的TMBB-CS算法信道估计性能更好。      

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.     

联合波束域分解和SVD的多用户大规模MIMO系统信道估计

针对TDD(Time Division Duplex)模式下的多用户大规模MIMO(Multiple-Input Multiple-Output)系统,本文研究了将波束域分解和SVD(Singular Value Decomposition)同时用于该系统的信道估计。当基站天线数目较多时,信道估计误差、导频开销、信道估计算法的复杂度等问题将成为影响大规模MIMO系统性能的关键因素。运用波束域分解理论,将多用户的大规模MIMO系统分解成多个单用户的大规模MIMO系统,同时从波束域对信道建模,该方法降低导频开销的同时也减小了信道估计误差。另外运用SVD对信道自相关矩阵优化,可以进一步降低信道估计算法的复杂度。基于以上两点,本文提出了一种联合波束域分解和SVD的大规模MIMO信道估计方案,并推导出了估计误差协方差矩阵的闭式表达式。仿真结果表明,与同类方案相比,本文提出的方案具有更好的信道估计性能。      

Low complexity joint estimation of synchronization impairments in sparse channel for MIMO–OFDM system

Low complexity joint estimation of synchronization impairments and channel in a single-user MIMO–OFDM system is presented in this paper. Based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset, sampling frequency offset, and symbol timing error, and channel, a Maximum Likelihood (ML) algorithm for the joint estimation is proposed. To reduce the complexity of ML grid search, the number of received signal samples used for estimation need to be reduced. The conventional channel estimation techniques using Least-Squares (LS) or Maximum a posteriori (MAP) methods fail for the reduced sample under-determined system, which results in poor performance of the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS) based channel estimation method in a sparse fading scenario, where the received samples used for estimation are less than that required for an LS or MAP based estimation. The performance of the estimation method is studied through numerical simulations, and it is observed that CS based joint estimator performs better than LS and MAP based joint estimator.