混合分集多天线系统基于相关性的分析信道建模

在考虑正交极化子信道之间相关性的前提下,指出传统分析模型无法体现极化方向对相关性的影响,并提出一种新的建模理论对传统模型进行修改补充;然后指出传统模型中使用天线在距离分集和极化分集情况下的相关系数来计算混合分集情况下的相关系数是一种不够准确的近似方法,提出直接利用天线处于混合分集情况下的统计数据进行建模,这种新的模型可以准确复现子信道间的相关系数,满足高精度的需求. 关键词： 多输入多输出系统(MIMO) 信道模型 分析模型 相关系数     

多波束分集深海远程正交频分复用水声通信

针对深海远程信道传播衰减大、多途扩展严重的问题,提出一种单输入多输出（SIMO）模型下的水声正交频分复用（OFDM）多波束空间分集均衡方法。建立了深海远程信道下SIMO-OFDM接收信号模型,对宽带波束形成处理后的多个到达角度的波束输出进行独立的多普勒补偿和稀疏信道估计,最后基于最大比合并实现多波束均衡。相对于远程通信中常用的单波束处理方法,所提方法可获得额外的不同到达角度的多径分集增益;且基于阵列波束形成处理可明显提升各个波束输出信噪比,避免了单阵元信噪比过低导致的信道估计误差增大的问题。所提方法的计算复杂度大于单波束均衡,但低于多阵元最大比合并均衡。基于BELLHOP的数值仿真和中国南海实验结果表明,所提方法的误码率性能明显优于单波束均衡和最大比合并均衡,且在100 km的距离上实现了通信速率199 bps的无误码通信。      

基于UKF的多用户混沌通信

为克服信道噪声、系统参数误配及多用户干扰对混沌通信系统的影响,本文组合不同的状态空间模型并结合盲提取算法,提出了一种双无先导卡尔曼滤波器 (dual unscented Kalman filter, DUKF),以实现多用户的混沌通信.仿真结果表明,在多输入多输出信道的多用户通信环境下,该算法有较快的收敛速度,并能有效地实现多用户的混沌通信. 关键词： 混沌通信 多输入多输出 双无先导卡尔曼滤波器 盲提取     

基于信道处理的时间反演幅度可调控多目标聚焦方法

在微波无线输能领域中,如何实现多目标点的电磁波可调控聚焦是一个值得关注的问题.本文提出了一种基于时间反演多径环境下的多目标电磁波聚焦的新方法.该方法基于多个输出之间的信道相关性,将输入和输出节点之间的信道信息进行提取、筛选、加权和重构后在单个发送端上重建反演信号,利用时间反演的空间选择特性实现均衡的电磁波聚焦.基于这种方法,设计了两组在多径环境下的实验.实验结果表明,通过这种方法可以使弱相关模型下不同输出端口获得均衡稳定的聚焦峰,在强相关模型下使不同输出端口的分辨效果进一步提升.此外,6个额外的实验验证了所提出的方法可以在弱相关或强相关的单输入多输出信道模型下,通过改变不同的权值灵活地调整不同接收端的输出峰值电压比.     

基于空间光调制器的MIMO大气传输特性研究

大气湍流对无线光通信系统的影响不可忽视,为了更准确地反映实验室模拟多输入、多输出(MIMO)大气湍流信道的实际特征,提出了一种利用相位屏来模拟MIMO大气湍流信道的方法,并针对基于液晶空间光调制器（LC-SLM）的液晶调制法展开研究,通过实验验证该方法的可行性。实验结果表明：通过相位屏模拟MIMO大气湍流信道的激光光斑发生不同程度的畸变,湍流环境下两路激光发射系统比单路发射激光系统功率稳定性好,在前向纠错误差极限（3.8×10-3）下,单个发射单个接收系统的链路代价为10.5 dB,2个发射2个接收的MIMO系统的链路代价为9.3 dB。该项研究对于实验室模拟MIMO大气湍流信道实验方法提供一种新思路。     

自适应功率分配可见光MIMO信道容量研究

可见光通信（VLC）系统通常采用多阵列光源布局方式来兼顾照明与通信双重功能,因此需要使用多输入多输出（MIMO）技术进行多天线协同传输来实现高速率通信。然而传统MIMO系统中采用平均功率分配来实现空间复用,无法充分体现MIMO多天线协调传输的优势。根据每组收发天线信道状态的差异,设计了一种低计算复杂度的快速迭代注水算法,可实现依据信道特征自适应的分配信息,从而提高系统的信道容量。仿真结果表明:在相同信噪比情况下注水算法自适应功率分配系统比等功率分配系统的信道容量提高了1.25 bit/Hz左右。     

多输入多输出与时反联合的波导声呐有源目标定位

为了提高对浅海静止小目标的探测能力,提出了将多输入多输出和时反处理相结合的波导声呐处理框架。利用收发合置垂直阵,假设目标为点目标,每一个阵元轮流时反发射正交信号照射目标,整个阵接收对应的目标回波。当所有的阵元发射正交信号结束以后,对所有接收的目标回波数据进行分集处理,然后与时反发射驾驶向量做匹配滤波完成对目标的定位。分时发射策略克服了水声信道时延扩展和多普勒扩展导致常规多输入多输出处理接收端不同回波信号分离的困难。采用物理时反发射和数值时反接收聚焦,有助于抑制混响、提高回混比。数值仿真和波导水池实验结果表明多输入多输出时反处理较常规的有源匹配场处理以更高的精度对波导中的目标进行定位。      

基于DPIM调制的MIMO空间光通信系统

为了抑制空间光通信中大气湍流效应和降低误包率,提出了一种引入分布式多出多入技术,基于数字脉冲间隔调制的多出多入空间光通信系统.在弱湍流信道模型和APD探测器下建立了多出多入系统链路模型,推导了最大似然检测下的最佳阈值和误包率.计算结果表明:发射分集通过多路径传输平滑接收信号光强起伏;接收分集增加孔径平滑效应,减弱接收光强起伏;在发射平均功率、接收孔径总面积和背景噪音相同的条件下,数字脉冲间隔调制的不同多出多入系统存在几乎相同的最佳雪崩光电二极管增益;比较多出多入通信系统下三种调制方式,数字脉冲间隔调制的误包率较少劣于PPM调制而大大优于OOK调制.     

多输入多输出量子密钥分发信道容量研究

量子安全通信是一个量子密钥分发过程,目前采用的通信技术严重制约了量子密钥分发的比特率.将多输入多输出（MIMO）技术应用于量子密钥分发系统,可提高量子密钥分发的比特率,促进量子安全通信向高速大容量发展.文中首先构造出MIMO量子密钥分发信道中多光子纠缠态Wigner算符矩阵.并在此基础上,推导出多光子双模压缩纠缠态Wigner算符矩阵和MIMO量子密钥分发信道容量.为开发稳健的MIMO量子安全通信空时处理算法和优化设计高性能MIMO量子密钥分发系统提供理论支撑和技术基础. 关键词： 多输入多输出 双模压缩态 多光子纠缠态 信道容量     

多天线信道空间衰落相关性近似算法及其复杂性研究

信道空间衰落相关性(SFC)主要取决于波达信号的功率方位谱(PAS)和多天线阵列收发模式.深入研究了移动通信系统中多天线阵列SFC近似计算法及其复杂性.首先导出在典型PAS为均匀分布、高斯分布以及拉普拉斯分布下的SFC函数的闭合表达式.再研究在波达信号PAS小角度扩展时的近似计算法,建立多输入多输出(MIMO)多天线接收信道模型,深入分析所选择的天线阵列和电波传播参数对MIMO系统信道容量的影响.通过理论计算和仿真实验得出近似计算法在特定条件下具有很好的拟合度,定量分析了近似计算法在对MIMO多天线系统分析时的适用性和计算效率.该算法能极大地减低理论计算复杂性,提高分析和仿真MIMO多天线系统的效率.     

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.     

Secure communication over RIS-aided underlay CR MIMO wiretap channel without Eve’s CSI

This paper investigates a reconfigurable intelligent surface (RIS)-aided underlay cognitive radio (CR) multiple-input multiple-output (MIMO) wiretap channel where the secondary transmitter (ST) communicates with primary user (PU) and secondary user (SU) in the absence of the eavesdropper’s (Eve’s) channel state information (CSI). To enhance the secrecy performance in CR MIMO wiretap channel, the power of useful signal is minimized at ST, and then the residual power is further utilized to design artificial noise (AN) based on statistical CSI at ST. Specifically, we first optimize the transmit covariance matrix at ST and the diagonal phase-shifting matrix at RIS jointly leveraging large-system approximation results. Then the power allocation for SU is optimized to obtain the minimum transmit power of useful information at ST. Besides, we further design AN with the residual power by aligning it into the null space of the SU channel and thus avert the harmful effects of AN to improve the secure communication quality of SU. Finally, through numerical simulations, we illustrate the effectiveness of the proposed algorithm and validate the existence of a trade-off between the quality-of-service (QoS) at SU and secrecy rate.     

Characterization,modeling and simulation of the MIMO propagation channel

This article deals with several aspects relative to the MIMO propagation channel. Based on simulations and/or measurements, different approaches are used to model the propagation channel. These models are useful for the MIMO system design. Several studies are performed in order to realize realistic simulation of MIMO channel. Different measurement techniques are used in characterizing the propagation channel in various environments. Measurement campaigns made in different situations have been analyzed to obtain the relevant statistical parameters of the channel. Simulation of MIMO channel is then presented. Measurement and simulation results provide an evaluation of the capacity of MIMO channel. Obtained results show feasibility in the integration of MIMO techniques in practical wireless communication systems.     

Energy Efficiency of User-Centric,Cell-Free Massive MIMO-OFDM with Instantaneous CSI

In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.     

基于改进的统计信道模型与多天线系统性能分析

为提高电磁信号的到达角度以及多普勒效应等信道参数估计的准确性,在散射体非均匀分布下引入了散射概率和有效散射体两个物理概念,提出一种合理的改进型空间信道模型,该模型能准确地描述宏小区(macrocell)和微小区(microcell)等各种移动通信环境下的重要空时信道参数,并应用于多入多出系统(multiple input multiple output,MIMO)信道性能仿真中.数值仿真结果与早期多径衰落信道模型对比,表明本模型的信道参数估计结果符合理论和经验,拓展了空间统计信道模型的研究和应用,对评估多天线MIMO系统空时处理算法和仿真无线通信系统提供有力的工具.     

基于压缩感知理论的大规模MIMO系统下行信道估计中的导频优化理论分析与算法设计

针对大规模多输入多输出(multiple input multiple output, MIMO)系统信道估计中的导频设计问题,在压缩感知理论框架下,提出了一种基于信道重构错误率最小化的自适应自相关矩阵缩减参数导频优化算法.首先以信道重构错误率最小化为目标,推导了正交匹配追踪(orthogonal matching pursuit, OMP)算法下信道重构错误率与导频矩阵列相关性之间的关系,并得出优化导频矩阵的两点准则,即导频矩阵列相关性期望和方差最小化;然后研究了优化导频矩阵的方法,并提出相应的自适应自相关矩阵缩减参数导频矩阵优化算法,即在每次迭代过程中,以待优化矩阵平均列相关程度是否减小作为判断条件,调整自相关矩阵缩减参数值,使参数不断趋近于理论最优.仿真结果表明,与采用Gaussian矩阵、Elad方法、低幂平均列相关方法得到的导频矩阵相比,本文所提方法具有更好的列相关性,且具有更低的信道重构错误率.     

水声多输入多输出信道的分布式压缩感知估计

多输入多输出技术通过采用多个阵元进行多发多收空间复用信道可在极其有限的通信带宽下实现高速水声通信,但由于同时存在通道间干扰和多径干扰,水声MIMO信道估计变得困难。提出利用MIMO水声信道多径稀疏结构存在的相关性,在经典联合稀疏模型的基础上对MIMO观测矩阵进行重组,从而建立基于分布式压缩感知的单载波水声MIMO通信信道联合稀疏模型;同时,针对信道响应中具有相同多径位置的稀疏部分和特有稀疏部分设计区分性正交匹配追踪算法进行联合重构,进一步抑制通道间干扰的影响。最后通过仿真和海上实验进行本方法有效性的验证,实现16 kbps的MIMO水声通信。通过算法推导、仿真和实验可得到结论:利用MIMO水声信道多径相关性进行分布式压缩感知估计可提高估计性能。      

三维空间域多普勒功率谱及其多天线系统性能

在三维(3 dimensional,3D)空间域信道建模中,针对波达信号仰角(elevation angle,EA)在不同覆盖区散射体环境中的分布,提出了指数型EA仰角概率密度函数并对其信道特征实现建模.在假设波达信号方位谱为均匀分布时,采用近似算法在对称和非对称两种情况下导出其多普勒功率谱密度(power spectral density,PSD)闭合表达式.从分析结果可发现PSD与仰角函数式及波达信号边界角度参数βmin和βmax关系密切,并与多普勒频移密切相关.对该三维空间域中的多输入多输出(multiple input multiple output,MIMO)多天线信号衰落相关性(spatial fading correlation,SFC)进行了推导和仿真.结果表明MIMO多天线阵元间SFC与βmin和βmax关系密切,而EA仰角概率密度函数参数n对SFC影响较小.本文引入的指数型EA仰角概率密度函数可应用于多种无线通信环境下的信道参数估计,且与传统模型对比表明本模型的信道参数估计结果符合理论和经验值,拓展了对3D空间域统计信道的建模与MIMO多天线分析计算.     

Deep Task-Based Quantization

Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by processing the signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine their analog-to-digital mapping using deep learning tools. These mappings are designed to facilitate the task of recovering underlying information from the quantized signals. By using deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task.