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。      

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...     

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。      

混合逆狄利克雷分布的变分学习及应用

混合逆狄利克雷分布是正的非高斯数据分析中一个重要的统计模型.但是利用常用的统计方法比如极大近似然估计、矩估计等往往很难得到模型参数估计的显性解析式.本文提出一个变分贝叶斯学习算法,它能够在估计参数的同时自动确定混合分量数.在合成数据集及实测数据集上的实验结果表明利用变分贝叶斯推理来估计混合逆狄利克雷分布是一种非常有效的方法.     

频分双工大规模MIMO系统时变信道估计

大规模多输入多输出（Multiple-Input Multiple-Output, MIMO）系统的性能增益依赖可靠的信道估计,传统信道估计方案主要面向准静态场景,在用户高速移动场景中性能下降明显。本文研究频分双工（Frequency Division Duplex, FDD）大规模MIMO系统中的时变信道估计问题,利用信道向量在角度域的空时稀疏特性,提出软结构先验模型驱动的稀疏贝叶斯信道估计（Soft-Structured Prior Model based Sparse Bayesian Estimation, SSPM-SBE）方案,针对方案涉及的复杂贝叶斯估计问题,给出基于变分优化的低复杂度求解方法。SSPM-SBE方案能够充分利用当前和历史接收导频数据改善时变信道的估计性能,且无需信道大尺度信息的先验认知,仿真结果验证了方案的优越性。      

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。     

基于压缩感知的大规模MIMO下行信道状态信息获取

信道状态信息（Channel State Information,CSI）对于大规模多输入多输出（Multiple-Input Multiple-Output,MIMO）发挥高性能至关重要。但在上下行传输信道不存在互易性的频分双工（Frequency Division Duplex,FDD）制式下,若采用传统的信道估计方法会给CSI的获取带来巨大的导频开销和计算量。考虑利用大规模 MIMO 信道的虚角域稀疏性来减少获取CSI所需开销,在此基础上进一步研究了大规模 MIMO 正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中各子载波信道在虚角域的共同稀疏特性和稀疏支撑集的时间相关特性,达到降低信道维度的目的,则大大减少了基站对 CSI 获取所需的资源开销。同时,为了降低信道稀疏支撑集信息获取所需的导频开销和提高信息的时效性,利用压缩感知技术对支撑集进行估计。仿真结果验证了所提方案性能的优越性。     

基于透镜天线阵的毫米波系统波束选择研究

透镜天线阵列下毫米波大规模多输入多输出(MIMO)系统通过波束选择可以大大减少所需的射频链数目,解决系统硬件成本和能耗过高的问题,然而,波束选择需要基站端获取准确的信道状态信息.对此,文章首先利用波束空间的结构特性,采用基于支持检测(SD)的信道估计方案,以较低的导频开销和计算复杂度对大尺寸信道进行更加可靠的估计;其次...     

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。     

MIMO-OFDM系统的低复杂度递推信道跟踪算法

该文针对时变多径信道下的MIMO-OFDM系统,基于变分贝叶斯原理,提出了一种新的联合信号检测和信道跟踪的低复杂度半盲贝叶斯迭代接收机。针对该接收机,基于递推变分期望最大化(RVBEM)算法,提出了一种RVBEM信道跟踪算法。由于RVBEM算法需要进行矩阵求逆,因此以该算法为基础推导得到了一种时频域联合递推的低复杂度信道跟踪(TF-LCRVBEM)算法。TF-LCRVBEM算法不仅完全避免了矩阵求逆运算,还通过合理的近似使得算法只具有线性复杂度。分析和仿真表明,在时变多径信道下,所提迭代接收机具有远优于传统接收机和接近理想接收机的性能。     

基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计

本文首先讨论了大规模MIMO-OFDM（Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing）系统信道的空间相关性,提出了一种基于隐聚类假设的信道建模方法,利用概率参数模拟不同的传播环境.然后,将机器学习领域的狄利特雷过程（Dirichlet Process,DP）引入到稀疏贝叶斯学习（Sparse Bayesian Learning,SBL）模型中,建立了DP-SBL结构,在信道估计的同时挖掘并利用大规模MIMO系统所特有的隐聚类特征.接着,将DP-SBL结构应用于大规模MIMO-OFDM系统中,在因子图上利用消息传递算法推导了一种基于隐聚类和狄利特雷过程的接收机算法.最后,将本文提出的接收机算法和现有算法进行对比分析.结果表明,本文提出的接收机算法充分利用了大规模MIMO-OFDM系统特有的空间相关性,能够以较低的计算复杂度获得较强的鲁棒性和显著的性能增益.     

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.     

OFDM稀疏信道估计中基于树状随机搜索导频设计新方法

压缩感知（CS,Compressed Sensing）是一种以低速率对稀疏信号进行采样后在接收端重建信号的技术,基于CS的稀疏信道估计具有更小的导频开销且具有更好的信道估计性能。针对基于CS的OFDM稀疏信道估计中的导频设计问题,提出一种基于树状随机搜索算法（TSS,Tree-based Stochastic Search Algorithm）的导频位置设计新方法,该方法结合了树的结构,以分支的方式进行随机搜索从而避免陷入局部最优问题。仿真结果表明,与传统的导频设计方法相比,使用TSS算法获得的导频图案用于信道估计中能够获得更优的信道估计性能,而且TSS算法的复杂度更低。      

认知无线电中的稀疏信道估计与导频优化

认知无线电技术能充分利用闲置的频谱进行数据传输,从而提高频谱利用率。而稀疏信道估计能充分发掘无线信道的稀疏性,从而节省导频开销,并进一步提高频谱利用率。因此,该文研究了采用稀疏信道估计的认知无线电系统及导频优化,将信道估计转化为稀疏重建问题,以最小化观测矩阵的互相关为目标进行优化,并提出了一种快速的导频优化算法。该算法通过灵活设置外循环和内循环次数,实现了对导频序列进行逐位置的替换与优化。仿真结果表明,相比于最小二乘信道估计,稀疏信道估计能节省72.4%的导频开销,提高8.2%的频谱利用率;此外,该导频优化算法优于目前的随机优化算法,在相同的0.012误码率性能下,相比后者能节省约5 dB的信噪比。     

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.     

Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems

Massive MIMO (multiple-input-multiple-output) is one of the key technologies of 5G mobile cellular networks, which can form a huge antenna array by providing a large number of antennas at the cell base station. It will greatly improve the channel capacity and spectrum utilization and has become a hotspot in the field of wireless communications in recent years. Aiming at the high complexity of channel estimation algorithm for massive MIMO system, a sparse channel estimation algorithm with low complexity is proposed based on the inherent sparsity of wireless communication channel. The algorithm separates the channel taps from the noise space on the basis of the traditional discrete Fourier transform (DFT) channel estimation, so that the channel estimation only needs to calculate the part of the channel tap, so the computational complexity of the algorithm is greatly reduced. The simulation results show that the proposed algorithm can achieve near minimum mean square error (MMSE) performance while maintaining low complexity. Moreover, the Bit Error Rate and Inter-Cell Interference also indicates that the proposed improved algorithm shows better overall performance than the conventional algorithms which makes it suitable from practical perspective.