基于信号共轭周期平稳特性的MIMO信道估计方法

MIMO系统中,由于各天线间干扰以及码间干扰等问题的存在,信道估计性能成为影响系统性能的决定性因素。直接利用接收信号的二阶统计特性进行信道估计是目前比较通用的方法之一。本文基于信号的二阶共轭周期平稳特性。提出了一种适用于MIMO系统的信道估计方法,该方法直接利用接收信号的共轭周期自相关函数间的关系,消除了系统的码间干扰(ISI)和信道间的互扰(CCI),分离出信道矩阵的各个元素分别加以估计。该估计算法不需要事先知道信道的准确阶数,整个估计结果不受信道阶数过估计的影响。由于一般的信号不具有共轭周期平稳特性,因而本文同时还提出了一种在MIMO系统中构造和处理二阶共轭周期平稳特性信号的方法。本文最后给出的仿真结果显示所提的估计方法具有良好的估计性能。     

基于包络信息的BPSK 信号与侦收信道带宽定量关系分析

针对二相编码信号经过数字信道化接收机会产生包络缺口现象,通过改变了信号与接收机相关参数,发现对包络缺口个数正确估计的条件要强于对脉宽的估计,且位于信道边沿的信号不能保证包络缺口个数的正确估计。该研究以包络缺口个数与二相编码相位跳变点相等,脉宽估计相对误差小于10%作为正确接收的条件,得到了信号带宽与信道带宽之间的定量关系:当信号载频与信道中心的距离不大于信道带宽的44. 4% 时,可正确接收的信号带宽为信道带宽的0. 493;当距离不大于信道带宽的37. 2% 时,可正确接收的信号带宽增大至信道带宽的0. 704;该结果对不同的编码方式依然成立。     

MIMO OFDM系统中同步和信道的联合估计

提出了一种MIMO OFDM系统的定时恢复、频率同步和信道估计的联合算法。为了减少算法的复杂度，算法分两步完成：首先利用接收信号的自相关函数进行粗同步和信道估计，得到时延和频偏的粗估计，然后在粗估计基础上采用最大似然准则进行精确的同步和信道估计。仿真结果表明，该算法能够达到很好的效果，系统误码率接近已知信道响应时的情况。     

变换域通信系统信号基函数的盲同步估计方法

为了解决当收发两端频谱环境不一致时,变换域通信系统(TDCS)基函数序列的盲同步估计问题,文章提出了一种TDCS信号盲同步的改进算法。相比于传统的基于F_范数盲同步法系统性能明显改善。将接收到的TDCS信号进行周期分段以形成连续多个观察向量,分段向量通过改进算法估计采样延时。同步后将接收信号按基函数周期分段得到分段向量,求协方差矩阵并累加平均,最后通过特征值分解法得到最大特征向量即估计的基函数序列。仿真结果表明:估计的基函数相关解调接收信号得到的误码率与收发两端基函数一致条件下的误码率基本一致。     

多径信号相关与信道相关阵列空间谱估计性能

移动通信信号传输过程中不仅存在多径相关或相干信号,多信道接收还存在信道相关性.仿真分析表明,相关信号的谱估计几乎无法分辨;针对信道相关性,引入了信道相关系数矩阵,分析信道相关性对阵列空间谱估计的影响,分析可知,信道相关性增加,阵列的空间谱分辨率显著恶化;文中在同时存在信号相关与信道相关的双重因素下,分析阵列空间谱的估计...     

基于子空间相关信道模型的大规模MIMO信道估计

考虑到大规模MIMO系统的信道向量近似正交以及大量可用自由度,可以对接收信号运用奇异值分解(SVD)理论进行信道估计,并且产生的模糊矩阵用一小段导频就可以完全解决。MIMO系统的性能依赖于传播环境的复杂度和所用天线阵列的性能,更依赖于实际信道所提供的自由度。因此介绍了一种信道模型,该模型中角域划分为有限的信号到达方向,也就是到达角(AOA),并考虑在上行链路中将优于特征值分解(EVD)的SVD运用在AOA相关信道模型上进行信道估计。仿真结果显示,相关信道模型下此估计器的估计精度与基于导频的方法相比可以实现大幅度提高,以达到提高大规模MIMO系统性能的目的。     

OFDM系统信道盲估计的一种新算法

利用循环前缀（CP）引入的信息冗余，在利用接受信号的二阶统计特性实现正交频分复用（OFDM）系统信道盲估计的子空间算法基础上，采用矩阵变换的方法，改善信息序列的非周期相关函数，推导出信道估计的新算法。     

相关Nakagami信道下信道估计器对RAKE接收机性能影响研究

在实际环境中RAKE接收机在接收信号时都要进行信道系数估计,以便进行相干合并。文章研究了不采用导频信号与采用导频信号两种估计器,具有估计误差情况下,在相关Nakagami信道下的RAKE接收机的性能,并且通过特征函数法得到了误码率公式的闭合表达式。比较了两种估计器对系统性能的影响,最后给出了数值结果,可以看出,当导频信号能量与信号能量相等,且导频数目比较多时（大于5）,采用导频信号的系统性能优于直接估计信道系数的系统。直接估计信道系数的系统性能在信噪比大于5dB后,其性能远远优于导频信号的估计系统。     

Rayleigh信道多天线系统的酉空时信号识别技术

针对衰落信道中酉空时调制的识别问题,提出两种酉空时信号与传统空时码的识别方案。最大似然识别法利用信道转移概率密度构造平均似然比和广义似然比分类函数,依据不同码字似然比的差异完成分类,在对数域处理从而降低计算复杂度。高阶统计特性识别法利用随机矩阵的矩生成函数产生高阶联合矩和高阶联合累积量,依据酉空时信号特殊的高阶统计特性实现识别。最大似然识别法可在无信道状态信息的条件下完成识别,当已知信道状态信息时识别性能可大幅提高;高阶统计特性识别法需要信道状态信息,同样条件下与最大似然法相比其性能较差,且其准确性会受信道估计的影响,但实现的复杂度低。通过增加接收天线数量在各种方案中均可改善识别性能,4根接收天线相对2根接收天线的增益,无CSI的最大似然法为7-10dB,有CSI的高阶统计特性法可达45dB。仿真结果验证了所提方案的有效性。      

认知无线电中OFDM信号信噪比盲估计

针对认知正交频分复用(OFDM,orthogonal frequency division multiplexing)系统中低信噪比多径信道下传统的OFDM信号信噪比盲估计算法的估计性能差,计算复杂度高的问题,提出一种新的OFDM信号信噪比盲估计方法,该方法首先利用自相关函数的特性粗略估计出信道阶数,确定循环前缀部分中不受符号间干扰的数据区间,然后根据选定区间的数据的自相关函数值估计接收信号的信号功率,最后利用循环前缀数据为部分有用数据的复制这一特性估计出噪声功率,从而估计出接收信号的信噪比。仿真实验结果表明,提出的方法无需任何先验信息,在低信噪比多径信道下具有良好的估计性能,且计算复杂度低,更适合于认知OFDM系统。     

Performance Investigation of Space‐Time Block Coded Multicarrier DS‐CDMA in Time‐Varying Channels

In this letter, we evaluate the system performance of a space‐time block coded (STBC) multicarrier (MC) DS‐CDMA system over a time selective fading channel, with imperfect channel knowledge. The average bit error rate impairment due to imperfect channel information is investigated by taking into account the effect of the STBC position. We consider two schemes: STBC after spreading and STBC before spreading in the MC DS‐CDMA system. In the scheme with STBC after spreading, STBC is performed at the chip level; in the scheme with STBC before spreading, STBC is performed at the symbol level. We found that these two schemes have various channel estimation errors, and that the system with STBC before spreading is more sensitive to channel estimation than the system with STBC after spreading. Furthermore, derived results prove that a high spreading factor (SF) in the MC DS‐CDMA system with STBC before spreading leads to high channel estimation error, whereas for a system with STBC after spreading this statement is not true.     

相关衰落信道MIMO STBC分集增益与多用户自适应OFDMA研究

首先沿用经典的分集增益特性分析了在已知与未知多输入多输出（MIMO）信道状况的条件下空时分组码（STBC）接收机的性能,从而揭示了相关衰落下信道矩阵的秩和特征值扩展对分集增益与编码增益等极其重要的影响;接着提出了一种与多天线STBC等效的单发单收（SISO）模型,将复杂的分集增益指标转换为直观的SNR指标,明确表示出接收机SNR与信道相关矩阵之间的关系,并以此为基础,提出一种实用的多用户MIMO-OFDM系统STBC编码优化的子载波资源分配OFDMA方法,进一步提高频谱效率与系统性能.     

Full-Diversity Codes for MISO Systems Equipped With Linear or ML Detectors

In this paper, a general criterion for space-time block codes (STBC) to achieve full diversity with a linear receiver is proposed for a wireless communication system having multiple transmitter and single receiver antennas [multiple-input–single-output (MISO)]. Particularly, the STBC with Toeplitz structure satisfies this criterion, and therefore, enables full diversity. Further examination of this Toeplitz STBC reveals the following important properties: 1) the symbol transmission rate can be made to approach unity; 2) applying the Toeplitz code to any signalling scheme having nonzero distance between the nearest constellation points results in a nonvanishing determinant. In addition, if quadratic-amplitude modulation (QAM) is used as the signalling scheme, then for independent MISO flat-fading channels, the Toeplitz codes is proved to approach the optimal diversity-versus-multiplexing tradeoff with a zero-forcing (ZF) receiver when the number of channel uses is large. This is, so far, the first nonorthogonal STBC shown to achieve the optimal tradeoff for such a receiver. On the other hand, when maximum-likelihood (ML) detection is employed in a MISO system, the Toeplitz STBC achieves the maximum coding gain for independent channels. When the channel fading coefficients are correlated, the inherent transmission matrix in the Toeplitz STBC can be designed to minimize the average worst case pairwise error probability.      

实现MIMO系统定向干扰抑制的迭代MLD方案

收发多天线(Multiple Input Multiple Output,MIMO)空时分组编码(Space-Time Block Codes,STBG)系统中存在定向干扰,特别是军用通信环境下存在强定向干扰时,需进行有效的干扰抑制以避免性能恶化.本文提出了一种迭代最大似然解码(Maximum-Likelihood D...     

基于BP神经网络的空时分组码识别算法

针对多输入多输出（Multiple Input Multiple Output,MIMO）系统中空时分组码（Space-Time Block Code,STBC）的盲识别问题,提出了一种基于BP神经网络的空时码识别算法。首先对先前学者提出的基于空时相关矩阵的F范数在新的空时码集下的区分性进行了验证,并基于该范数设计了用于空时码识别的六维特征,最后使用BP神经网络对提取的六维特征进行分类以获得识别结果。相比于传统算法,本文算法可识别的空时码集更大;相比于深度学习的算法,本文算法在较为恶劣的瑞利信道下具有更高的识别率。仿真结果表明,所提算法在信噪比为10 dB时可达95%以上的识别率,且算法对不同的调制方式及不同程度的定时同步误差均具有较好的鲁棒性,识别过程无需要对信道信息进行预估计,在电子对抗等非协作场景下具有较好的应用价值。     

基于准正交空时码的最优线性预编码矩阵设计

为了充分利用信道信息改善应用准正交空时分组码的MIMO系统的性能,该文提出了将预编码矩阵和准正交空时分组码相结合的发射机制。信息在经过准正交空时分组码编码后,要经过线性预编码矩阵进行处理,才可以从发射天线发送。文中的预编码矩阵设计利用了MIMO信道的非零均值矩阵和相关矩阵,并采用成对差错概率作为其最优设计准则。仿真结果表明,相对于未经过预编码矩阵的系统,新系统的误码率性能有明显改善。     

Capacity Analysis of Adaptive Transmission with Space–Time Block Codes in Spatially Correlated MIMO Nakagami-m Fading Channels

In this paper we investigate the impacts of spatial fading correlation on channel capacity of orthogonal space–time block codes (STBCs) over multiple-input multiple-output Nakagami- \(m\) fading under different power and rate allocation policy. This paper presents the single-input single-output equivalency of STBC which is equivalent to scalar additive white Gaussian noise channel, with a channel gain proportional to the Frobenius norm of the matrix channel. We derive the characteristic function (c.f.) of the signal-to-noise ratio at the output of the STBC decoder, when spatial fading correlation is assumed. The c.f. turns out to be a linear combination of Laplace transforms weighted by the eigenvalues of the channel correlation matrix, where each element corresponds to an elementary Gamma probability density function. Based on this approach, and using the linearity of the Laplace transform, the capacity expressions are derived for STBC under different adaptive transmission policy. Numerical results illustrate the impact of fading correlation and fading severity on the channel capacity and outline the performance differences between the distinctive adaptation policies.     

Channel identifiability under orthogonal space-time coded modulations without training

Space-time block coded (STBC) transmission has been established as an efficient tool to enhance communication performance over wireless fading channels. The success of STBC decoding relies on accurate channel knowledge at receivers. In this work, we present a channel estimation approach that does not require training data to estimate unknown channels. Focusing on STBC from orthogonal designs, we present channel identification conditions that are largely verifiable in terms of the code and the antenna array configuration. We also develop a simple subspace-based algorithm to identify the unknown space-time channel matrix for complex transmission. Finally, we present simulation test results to illustrate the performance of the proposed method.     

短波单载波空时分组码的频域均衡研究

短波通信受多径衰落、干扰复杂等影响严重。空时分组码(Space Time Block Code,STBC)技术在无需增加频谱资源和天线发射功率的前提下,可以利用多输入多输出(Multiple-Input Multiple-Output,MIMO)信道提供的分集增益提升传输可靠性。分析短波MIMO研究现状,提出短波单载波STBC频域均衡(Frequency-Domain Equalization,FDE)系统架构,针对短波信道引入的码间干扰研究MIMO MMSE-FDE均衡技术,并将单载波STBC频域均衡与时域均衡及短波现有波形进行仿真对比。仿真结果表明,相较于短波现有波形,单载波STBC频域均衡系统的可靠性有较大幅度提升,且性能与STBC时域均衡接近,但计算复杂度远低于STBC时域均衡。     

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.