Impact of channel estimation error on channel capacity of multiple input multiple output system

In order to investigate the impact of channel estimation error on channel capacity of multiple input multiple output (MIMO) system, a novel method is proposed to explore the channel capacity in correlated Rayleigh fading environment. A system model is constructed based on the channel estimation error at receiver side. Using the properties of Wishart distribution, the lower bound of the channel capacity is derived when the MIMO channel is of full rank. Then a method is proposed to select the optimum set of transmit antennas based on the lower bound of the mean channel capacity. The novel method can be easily implemented with low computational complexity. The simulation results show that the channel capacity of MIMO system is sensitive to channel estimation error, and is maximized when the signal-to-noise ratio increases to a certain point. Proper selection of transmit antennas can increase the channel capacity of MIMO system by about 1 bit/s in a flat fading environment with deficient rank of channel matrix.

Channel capacity of multiple-input multiple-output systems with transmit and receive correlation

In order to investigate the impact of channel model parameters on the channel capacity of a multipleinput multiple-output （MIMO） system, a novel method is proposed to explore the channel capacity under Rayleigh fiat fading with correlated transmit and receive antennas. The optimal transmitting direction which can achieve maximum channel capacity is derived using random matrices theory. In addition, the closed-form expression for the channel capacity of MIMO systems is given by utilizing the properties of Wishart distribution when SNR is high. Computer simulation results show that the channel capacity is maximized when the antenna spacing increases to a certain point, and furthermore, the larger the scattering angle is, the more quickly the channel capacity converges to its maximum. At high SNR （〉12 dB）, the estimation of capacity is close to its true wlue. And, when the same array configuration is adopted both at the transmitter and the receiver, the UCA yields higher channel capacity than ULA.

Hybrid pilots assisted channel estimation algorithm for MIMO-OFDM systems

A hybrid pilots assisted channel estimation algorithm for multiple input multiple output（MIMO） orthogonal frequency division multiplexing（OFDM） systems under low signal-to-noise ratio（SNR） and arbitrary Doppler spread scenarios is proposed.Motivated by the dissatisfactory performance of the optimal pilots（OPs） designed under static channels over multiple OFDM symbols imposed by fast fading channels,the proposed scheme first assumes that the virtual pilot tones superimposed at data locations over specific subcarriers are transmitted from all antennas,then the virtual received pilot signals at the corresponding locations can be obtained by making full use of the time and frequency domain correlations of the frequency responses of the time varying dispersive fading channels and the received signals at pilot subcarriers,finally the channel parameters are derived from the combination of the real and virtual received pilot signals over one OFDM symbol based on least square（LS） criterion.Simulation results illustrate that the proposed method is insensitive to Doppler spread and can effectively ameliorate the mean square error（MSE） floor inherent to the previous method,meanwhile its performance outmatches that of OPs at low SNR region under static channels.

Effects of Input Redundancy on Time Optimal Control

由于有输入冗余性的系统的流行，这份报纸与输入冗余性集中于这些问题，在我们关于增加新输入冗余性进可控制的系统的效果担心的地方。预定最佳的控制问题被讨论，在如此的效果到最佳的时间被评估的地方。基于存在的假设和最佳的控制的唯一，如果，纸证明增加输入冗余性的数字将从一样的起始的状态导致最佳的时间的严格的减小在那里存在在冗余的输入隧道之中的非闲散的隧道。而且如果这个问题是正常的，那么所有冗余的输入隧道被用来弄短最佳的时间。在另一方面没有规度的假设，最佳的时间将也作为如果至少一条这些冗余的输入隧道是完全可控制的，比作原来的系统为冗余的系统是更小的。最后，二个数字例子被部署表明这份报纸的主要结果。

Mobile channel estimation for MU-MIMO systems using KL expansion based extrapolation

In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.

MIMO Channel Measurement and Characterization at 6.0-6.4 GHz Under Typical Classroom Environment

An extensive 4 x 4 MIMO channel measurement is carried out at 6. 0-6. 4 GHz under a typical classroom environment with channel sounder based on vector network analyzer. Both LOS and NLOS scenarios are considered. The results on path loss, delay spread and spatial correlation are presented. The measurement shows that, for corridor coverage, 2x2 MIMO is more economical than 4x4 MIMO due to high correlation. In order to identify the unique characteristics at the high frequency band, the measured channel parameters at 6. 0-6.4 GHz are compared with those at 2. 45 GHz. The comparison shows that the shortened wavelength of this higher frequency band results in a great difference of channel characteristics. Therefore, our measurement results provide new gnidance for the design and development of the system working on 6. 0-6.4 GHz band.

Factor-graph-based iterative channel estimation and signal detection algorithm over time-varying frequency-selective fading channels

The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.

Impact of Depolarization Phenomena on Polarized MIMO Channel Performances

The performance and capacity of multiple-input multiple-output (MIMO) wireless channels are limited by the spatial fading correlation between antenna elements. This limitation is due to the use of mono polarized antennas at receiver and transmitter sides. In this paper, in order to reduce the antenna correlation, the polarization diversity technique is employed. Although the spatial antenna correlation is attenuated for multi-polarization configurations, the cross-polar components appear. This paper highlights the impact of depolarization effect on the MIMO channel capacity for a 4×4 uniform linear antenna array. We assume that the channel is unknown at the transmitter and perfectly known at the receiver so that equal power is distributed to each of the transmit antennas. The numerical results illustrate that for low depolarization and spatial correlation, the capacity of single-polarization configuration behaves better than that of multi-polarization configuration.

Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis

The lattice-reduction （LR） has been developed to im- prove the performance of the zero-forcing （ZF） precoder in multiple input multiple output （MIMO） systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter （CSIT）, an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz （LLL） algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error （LMMSE） pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate （BER）. Assuming a time division duplexing （TDD） MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying （QPSK） modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients.

Evaluation of Preamble Based Channel Estimation for MIMO-FBMC Systems

Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing （OFDM） systems. However, because of or-thogonality in real field and the presence of imaginary intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper, we propose a channel estimation method which employs intrinsic interference cancellation at the transmitter side. The simulation results show that this method has less pilot overhead, less peak to average power ratio （PAPR）, better bit error rate （BER）, and better mean square error （MSE） performance compared to the well-known intrinsic approximation methods （IAM）.

面向6G的信道状态信息压缩技术

提出基于人工智能的信道状态信息压缩技术方案,并全面思考了模型训练、模型管理中的标准制定、实际应用等相关问题,分析了不同问题的潜在解决方案。仿真结果表明,所提出的方案可有效提升信道状态信息反馈精度,网络容量平均提升10%左右。

基于皮肤-脂肪模型的太赫兹体内多输入多输出信道特性分析与建模

为探究太赫兹(THz)频段体内多输入多输出(MIMO)通信系统的传输特性,该文在0.8~1.2 THz下构建了精确的皮肤-脂肪模型,对皮肤-脂肪模型中垂直方向和水平方向的链路进行全波电磁仿真,分析太赫兹体内信道特性,建立路径损耗模型。首先,结合太赫兹频段人体组织的介电特性和人体皮肤的解剖学结构构建皮肤-脂肪模型。其次,对比分析了3条链路的路径损耗和阴影衰落,提出带有等效吸收因子的太赫兹体内路径损耗模型。最后,对3条链路的莱斯K因子、均方根时延扩展、MIMO容量进行分析。仿真分析表明,带有等效吸收因子的太赫兹体内路径损耗模型可以更准确地描述加长距离垂直链路2的路径损耗,发射端在体表可以增强MIMO容量。该文的工作可以为太赫兹体内通信系统的设计和优化提供参考。

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。

空间分集光密钥生成的信道相关性影响

利用无线光信道本身的特性实现光密钥生成和分发,可为高速星地保密通信提供安全和高效的保障。开展了无线光信道空间分集密钥生成系统相邻子信道相关性的研究。通过实现功率谱反演产生的Kolmogorov大气湍流相位屏,构建了无线光信道(WOC)信道中的多输入多输出(MIMO)模型,采用蒙特卡罗模拟了高斯光束经大气湍流传输特性,比较了不同湍流强度和光束传输距离对光强分布的影响,分析了大气湍流强度、相邻两接收孔径之间的距离、接收孔径、传输距离、光波波长对相邻子信道相关性的影响。结果表明,相邻两接收孔径接收到的光信号功率的相关性随相邻两接收孔径的直径和波长的增大而增加,随相邻两接收孔径之间的距离、光波的传输距离、湍流强度的增加而减小。在大气折射率结构常数C_(n)^(2)为5×10^(-14)m^(-2/3)、接收口径为0.03 m、传输距离为2000 m的近地水平链路条件下,相邻信道之间的距离在0.07 m时接收到的光信号功率的相关性近似为零,此时两个相邻信道彼此独立。该结果可为建立空间分集大气光信道密钥提取系统提供参考。

低复杂度的大规模MIMO分布式信道估计

针对大规模多输入多输出(multiple-input multiple-output,MIMO)系统传统信道矩阵获取方式导频开销大、计算复杂度高的问题,提出了一种低复杂度的二阶段分布式信道估计方案。该方案的初始阶段在基站侧采用传统压缩感知算法恢复信道矩阵,第2阶段在用户端利用信道的时间相关性,将大规模MIMO的角度域信道分解为密集部分和稀疏部分,并分别估计以实现连续信道追踪。稀疏部分信道通过所提的分布式自适应弱匹配追踪(distributed adaptive weak matching pursuit,DAWMP)算法,利用子信道的联合稀疏性进行多维重建。相比于线性最小均方误差(linear minimum mean square error,LMMSE)算法,所提方案的信道分解策略有效减少了在用户端进行信道估计的计算复杂度。仿真结果表明,所提算法与经典压缩感知信道估计算法相比,计算复杂度降低了约33%,算法性能提升了约0.5 dB。

Improved H-infinity channel estimator based on EM for MIMO-OFDM systems

H-infinity estimator is generally implemented in timevariant state-space models, but it leads to high complexity when the model is used for multiple input multiple output with orthogo- hal frequency division multiplexing （MIMO-OFDM） systems. Thus, an H-infinity estimator over time-invariant system models is pro- posed, which modifies the Krein space accordingly. In order to avoid the large matrix inversion and multiplication required in each OFDM symbol from different transmit antennas, expectation maximization （EM） is developed to reduce the high computational load. Joint estimation over multiple OFDM symbols is used to resist the high pilot overhead generated by the increasing number of transmit antennas. Finally, the performance of the proposed estimator is enhanced via an angle-domain process. Through performance analysis and simulation experiments, it is indicated that the pro- posed algorithm has a better mean square error （MSE） and bit error rate （BER） performance than the optimal least square （LS） estimator. Joint estimation over multiple OFDM symbols can not only reduce the pilot overhead but also promote the channel performance. What is more, an obvious improvement can be obtained by using the angle-domain filter.

Novel Channel Estimation Method in Broadband MIMO-OFDM Systems

A least square （IS） parametric channel estimation method in broadband mt/ltiple input multiple output （MIMO） orthogonal frequency division multiplexing （OFDM） systems is proposed. The mean square error （MSE） performance using optimal training pilots is also given, which proves the method can improve the estimation precision greatly in sparse channel.. Since such method needs the multi-path time delays information of the channel, the probabilistic data association （PDA） method is employed to estimate the time delay of each path. Simulation results show that both the bit error rate （BER） and the MSE performance of the proposed method are better than the traditional LS channel estimation method.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

基于双模式分解多通道输入的VSC-STATCOM逆变器故障诊断模型

针对传统电压源型静止同步补偿器中逆变器故障诊断存在的信号特征提取不充分,深度学习网络识别能力不足以及高噪声情况下识别率较低等问题,提出了一种基于双模式分解、多通道输入(MCI)、并行卷积神经网络(PCNN)、双向长短时记忆(BiLSTM)网络和自注意力(SA)机制组合的逆变器故障诊断方法。首先利用变分模态分解和时变滤波经验模态分解对逆变器输出的三相电流进行分解,降低原始信号复杂程度,实现不同模态分量间的规律互补;其次,利用MCI-PCNN-BiLSTM-SA组合模型对特征矩阵进行深层特征提取、学习和识别;最后,通过仿真进行验证,结果表明所提方法特征提取能力较强,在无噪声情况下平均识别率高达99.48%,在高噪声情况下平均识别率达95.59%。

基于鲸鱼优化和并联深度学习模型的光伏功率超短期预测

对光伏发电功率进行准确的预测可以为制定调度计划提供参考,降低光伏发电随机性、波动性等特性产生的负面影响,提高并网时电网运行的稳定性和可靠性。为提高预测精度,提出一种相似日聚类、多模式分解、多支路多通道输入的深度学习模型和鲸鱼优化相结合的预测方法,用于不同天气类型的超短期光伏发电功率预测。首先,对光伏发电功率与各天气变量之间进行双相关性分析,筛选出重要天气变量,根据双相关性的结果构造日特征向量,使用围绕中心点划分(Partitioning around medoids,PAM)实现相似日聚类;然后,使用完全集合经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)、奇异谱分解(Singular spectrum decomposition,SSD)和变分模态分解(Variational modal decomposition,VMD)对重要天气变量与光伏发电功率进行多重分解,得到多尺度、信息互补的多通道空间特征矩阵,并由同时间段的原始序列得到多通道时序特征矩阵;最后,基于并联卷积神经网络(Parallel convolutional neural networks,PCNN)、双向长短期记忆网络(Bidirectional long short-term memory network,BiLSTM)建立并联多通道输入(Parallel multichannel input,PMCI)的组合深度学习预测模型(PMCI-PCNN-BiLSTM),通过鲸鱼优化算法(Whale optimization algorithm,WOA)对组合预测模型进行参数寻优,以多通道空间特征矩阵、多通道时序特征矩阵作为PCNN模块的输入、BiLSTM模块的输入,将提取到的空间特征与时序特征融合后作为BiLSTM预测模块的输入,在双向时空分析的基础上得到最终预测结果。经过实验验证,提出的组合预测方法对超短期光伏发电功率预测具有较好的预测稳定性和精度。