MIMO系统中一种鲁棒的叠加导频设计方法

与传统的复用式导频相比,叠加导频不占用系统的带宽资源,不会降低信息传输速率,因而受到越来越广泛的关注。基于叠加导频的一阶统计量信道估计算法与最小均方误差算法相比,避免了矩阵求逆运算,大大降低了接收机复杂度,但该算法对信道中的直流干扰非常敏感。本文基于一阶统计量算法,提出了一种优化的叠加导频设计方法,该设计方法在联合最优化信道估计均方误差,接收信干比和峰均功率比的同时,对直流干扰具有很好的鲁棒性。仿真进一步分析了导频参数与信道估计均方误差、接收信干比的关系,比较分析充分表明该设计方法在获得高接收信干比的同时具有良好的鲁棒性。     

一种MIMO系统中叠加导频的最优设计方法

与传统的复用式导频相比,叠加导频具有频带利用率高,传输效率高的优点,因而受到了广泛关注.本文基于叠加导频常用的一阶统计量信道估计算法,推导了MIMO(Multiple-Input Multiple-Output)系统中叠加导频参数与信道估计均方误差(Mean Square Error)和接收信干比(Signal to Interference and Noise Ratio)的闭合表达式.并进一步提出了一种叠加导频优化设计方法,使MIMO系统具有联合最优的信道估计均方误差,接收信干比和峰均功率比(Peak to Average Power Ratio).仿真分析了导频参数与信道估计均方误差和接收信干比的关系,表明了优化设计的有效性.     

大规模MIMO系统中基于用户位置和用户分类的导频分配

针对大规模MIMO通信系统中存在的导频污染问题,提出了一种基于用户位置和用户分类的导频分配方案。该方案根据路径损耗和到达角设计目标函数及合理阈值,通过将用户的目标函数与阈值进行比较,把系统中所有用户分为高干扰和低干扰两类,接着为低干扰用户复用导频序列、高干扰用户分配额外正交导频序列来减轻导频污染。仿真结果表明,该方案提高了通信系统性能。     

大规模MIMO系统中基于用户分类的动态导频分配

针对大规模MIMO系统导频污染问题,提出一种基于用户分类的动态导频分配策略.根据目标小区用户信号强度的方差设置合理的用户信号强度临界值,将目标小区用户分为中心用户和边缘用户.中心用户采用最大载干比算法分配导频,以最大化中心用户总信道容量.边缘用户采用贪婪算法分配导频,以提高每个边缘用户的信干噪比.仿真结果表明,所提策略在提高边缘用户信干噪比的同时能够有效提高系统的信道容量.     

大规模MIMO系统中的动态导频分配方案

针对多小区大规模多输入多输出(MIMO)系统中存在的导频污染问题,通过分析MMSE信道估计产生的误差,提出了一种动态导频分配方案.该方案将目标小区与相邻小区相邻的边缘区域内的用户进行动态导频分配,剩余区域内的用户进行随机导频分配来提升系统下行链路的平均可达和速率.仿真结果表明,该方法不仅能显著地提升信道估计的性能,而且还能有效地提高整个系统下行链路的可达和速率.     

大规模MIMO干扰和最小的相移导频方案

在大规模MIMO系统中,小区间使用合理相移后的导频序列而非复用同一组导频,能以导频长度的适当增加换取导频污染水平的下降。但已有研究在优化各小区导频相移量时未考虑小区间的实际干扰情况,而且在相移导频方案下传统基于贪婪搜索的协作式导频分配方法已不再适用。针对上述问题,选择与大尺度信道信息有关的系统总干扰最小作为优化目标,搜索得到更优的导频相位偏移值,降低导频污染水平;在所设计的相移导频方案基础上,采用低复杂度的禁忌搜索算法实现对各小区用户的协作式导频分配,进一步降低导频污染的影响。仿真实验表明,所设计的上行导频方案可以提高信道估计精度,进而提升下行用户传输速率。     

选择性叠加导频信道估计

叠加导频信道估计方法是将导频与信息数据直接叠加,不占用专门的频谱和时隙,估计算法灵活多样,能解决导频信道估计系统中频谱利用率低和盲估计中不能实时估计的问题。介绍了叠加导频的MIMO-OFDM系统,采用正交多相序列作为叠加导频,利用导频符号的自相关及导频和接收信号的互相关,提出了一种选择性叠加导频信道估计信道的算法。最后,通过理论分析和计算机仿真,表明该方法在快衰落环境中有良好的性能。     

基于预编码与导频分配的大规模MIMO导频污染抑制

导频污染问题是限制大规模多输入多输出(MIMO)系统性能的主要影响因素.针对这个问题,提出了一种基于改进预编码和最优导频分配策略的大规模MIMO系统导频污染抑制算法.首先,在系统下通过基于改进遗传优化算法的最大化信干噪比(SINR)预编码算法,获得最优预编码矩阵;然后,通过基于用户信道条件优劣的最优导频分配策略对每个小区用户进行导频分配,从而实现大规模MIMO系统导频污染抑制.通过Matlab仿真结果可知,相对于传统的SINR预编码算法,所提算法的复杂度降低了65%左右,而导频污染抑制性能提升了30%左右.该算法能够有效抑制导频污染,提升大规模MIMO系统的性能.     

一种OFDM系统中叠加导频的信道估计方法

提出了一种新的基于叠加导频的信道估计算法。利用循环序列频域能量集中在某些频点上的特点,消除未知传输数据对导频的影响,将导频完全从接收数据中分离出来。在此基础之上,结合PN序列的自相关特性,在时域进行信道估计,进一步降低了噪声对导频的影响。仿真结果表明,与传统方法相比,该方法不但误码率和均方误差更低,而且还具有计算复杂度低、频带利用率高的特点。     

无小区大规模MIMO系统中基于叠加导频的上行频谱效率理论上限分析

无小区大规模多输入多输出(Cell-Free Massive MIMO)突破传统蜂窝小区的概念,被认为是未来移动通信中十分富有潜力的技术.该技术通过部署大量的分布式接入点(Access Point,AP)以获得充分的宏分集增益,以提高用户服务质量.受信道相干时间的限制,系统中可供用户分配的正交导频资源非常有限,部分用户...     

莱斯衰落信道下大规模MIMO系统中的信道估计方法

日趋重要的高速移动工具,如高速铁路、无人驾驶飞机等,大多都处在开阔地带.由于视距传播的存在,瑞利衰落模型已经不能很好地描述该环境下的信道情况,而莱斯衰落信道模型由视距分量和多径分量组成,更能准确地表述上述信道变化.基于此模型,在大规模天线系统下,在已存在的基于叠加训练序列信道估计方法的基础上,提出了改进的信道估计方法和对应的解码方法.改进后的信道估计方法分为直射分量已知和未知两种情况,分别推导了相应的信道估计公式和解码方法.数值仿真结果验证了本文所提方案性能的优越性.     

Interference alignment scheme for massive MIMO system

The performance of massive multiple-input multiple-output （MIMO） system is limited by pilot contamination. To reduce the pilot contamination, uplink and downlink precoding algorithms are put forward based on interference alignment criterion. In the uplink receiving processing, the target function aligns the pilot contamination and the interference signals to the same null space and acquires the maximal space degree of the desired signals. The uplink receiving precoding matrix is solved on maximal signal to interference plus noise ratio （SINR） criterion considering the impact of the pilot contamination on channel estimations. The uplink receiving precoding matrix is used as the downlink transmitting precoding matrix. Exploiting the channel reciprocity, it is proved that, if the uplink receiving precoding matrix achieves maximal S1NR, the identical precoding matrix can be used in the downlink transmission and acquires maximal signal to leakage plus noise ratio （SLNR）. Simulations show that the spectrum efficiency of the proposed algorithm can reach about 1.5 times higher than that of popular matched filtering （MF） precoding algorithm, and about 1.1 times higher than multi-cell minimum mean square error （MMSE） precoding algorithm. The performance of the proposed algorithm can be improved approximately linearly with the increasing of the number of antennas.     

多输入多输出正交频分复用中基于频域的信干噪比估计

终端的信干噪比(SINR)信息是多输入多输出正交频分复用(MIMO OFDM)系统进行编码调制方式选择和资源分配的重要依据之一。在频率选择性衰落信道中,针对MIMO OFDM的链路特征提出一种低复杂度的SINR估计方法:利用子载波之间的相关性构造降秩矩阵,提取其协方差矩阵的最小特征值来估计SINR,并分析了SINR估计误差。遵循LTE标准规范,进行了计算机仿真。结果表明:在SINR小于30 dB时,该方法只需要利用连续4个参考信号载波,便可实现估计误差小于0.2 dB,且该方法对最大多普勒频移不敏感。     

一种基于信道估计的MIMO雷达目标角度估计方法

MIMO雷达是一种新体制雷达。它的信号传输矩阵包含了我们需要的目标信息,如角度信息。本文首先建立了包含目标角度信息的MIMO雷达接收信号模型。然后根据MIMO雷达接收信号的统计特性,提出了新的MIMO雷达传输信道的估计方法,然后从传输矩阵中估计出目标角度信息。最后,我们通过仿真实验验证了本文方法的有效性。     

基于干扰对齐的认知 MIMO 系统频谱共享与用户调度简

在认知MIMO多用户通信场景中,设计基于干扰对齐的信号处理算法,将认知信号与授权信号通过相互正交的子空间进行传输,实现认知用户对授权系统空闲空间信道的无冲突利用,并根据不同信道矩阵的空间传输性能的差异,实现合理的用户调度。仿真结果表明,所提方法能够有效利用空闲空间信道资源,获得多用户分集增益,在不影响授权业务的前提下提高认知用户的传输速率。     

基于两步噪声学习网络的波束域毫米波大规模MIMO信道估计

针对波束域毫米波大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统,构建了一种新型两步噪声学习网络（Two-step Noise Learning Network,TNLNet）。基本原理是在接收信号反复经过卷积层和池化层提取噪声特征的基础上,利用波束域毫米波大规模MIMO信道矩阵稀疏性所引起的相邻元素相近的特点,采用下采样将信道矩阵重构成4个子矩阵,提高训练测试效率。该算法具有以比全卷积去噪近似消息传递(Fully Convolutional Denoising Approximate Message Passing,FCDAMP)算法和学习去噪的近似消息传递(Learned Denoising-based Approximate Message Passing,LDAMP)算法更低的复杂度,取得了比最小二乘算法、最小均方误差算法、FCDAMP和LDAMP更优的归一化均方误差（Normalized Mean Squared Error,NMSE）性能;与快速灵活去噪卷积神经网络(Fast and Flexible Denoising convolutional neural Network,FFDNet)相比虽然复杂度略高,但具有更优的NMSE性能,且在单一训练模型中获得了比FFDNet更宽的信噪比适用范围,增强了实用性。     

最大化SINR 的MIMO 雷达发射协方差矩阵设计

对于由实际阵元功率放大器非线性特性引起的波形畸变问题,现有多输入多输出(MIMO)雷达发射协方差矩阵设计方法大都不能在解决的同时保证较高的输出信干噪比(SINR)。鉴于此,文中以最大化输出SINR 和保证MIMO 雷达高自由度为设计目标,结合二相编码(BPSK)波形满足恒模约束的特性,给出了一种满秩发射协方差矩阵,验证了提出的协方差矩阵可以用于生成BPSK 波形,并推导了其可获得的最大输出SINR。仿真结果表明,当干扰位置已知时,文中方法拥有较高的输出SINR,同时能够抑制更多的干扰。     

Randomized Kaczmarz algorithm for massive MIMO systems with channel estimation and spatial correlation

To exploit the benefits of massive multiple‐input multiple‐output (M‐MIMO) technology in scenarios where base stations (BSs) need to be cheap and equipped with simple hardware, the computational complexity of classical signal processing schemes for spatial multiplexing of users shall be reduced. This calls for suboptimal designs that perform well the combining/precoding steps and simultaneously achieve low computational complexities. An approach on the basis of the iterative Kaczmarz algorithm (KA) has been recently investigated, assuring well execution without the knowledge of second order moments of the wireless channels in the BS, and with easiness since no tuning parameters, besides the number of iterations, are required. In fact, the randomized version of KA (rKA) has been used in this context because of global convergence properties. Herein, modifications are proposed on this first rKA‐based attempt, aiming to improve its performance‐complexity trade‐off solution for M‐MIMO systems. We observe that long‐term channel effects degrade the rate of convergence of the rKA‐based schemes. This issue is then tackled herein by means of a hybrid rKA initialization proposal, which lands within the region of convexity of the algorithm and assures fairness to the communication system. The effectiveness of our proposal is illustrated through numerical results, which bring more realistic system conditions in terms of channel estimation and spatial correlation than those used so far. We also characterize the computational complexity of the proposed rKA scheme, deriving upper bounds for the number of iterations. A case study focused on a dense urban application scenario is used to gather new insights on the feasibility of the proposed scheme to cope with the inserted BS constraints.     

On channel estimation in cell-free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

This work addresses channel estimation (CE) in the uplink phase for a cell-free massive multiple-input multiple-output system operating under the time division duplex protocol. Considering that, channels are spatially correlated under the Rician fading model, where the investigated model is composed of two components: deterministic and nondeterministic, with the deterministic component describing the line-of-sight paths and the nondeterministic component describing the non-line-of-sight paths. Additionally, we dealt with correlated shadow fading that represents the most realistic situation. On the other hand, this work introduces a dynamic cooperation cluster framework in which the user is not served with the whole network ( i.e., all access points [APs]) but only the APs that present the best channel conditions regarding that user. In other words, this work proposes partial CE for each user because only APs with the best channel conditions are allowed to compute channel estimates. Consequently, we proposed partial channel estimators that perform the CE process with low complexity, namely, a partial minimum mean square error estimator and a partial element-wise minimum mean square error estimator. In addition, a simple pilot assignment technique is proposed in order to reduce interference signals so that each user experiences low interference from other users. Furthermore, the computational complexity required by each estimator is derived, where it is represented by the number of complex multiplications that each estimator requires in each consistency block. Theoretical and simulated results are provided, where the performance of each estimator is evaluated and analyzed using the normalized mean-square error metric.