Defected Ground Structure Multiple Input-Output Antenna For Wireless Applications

In this paper,the investigation of a novel compact 2×2,2×1,and 1×1 Ultra-Wide Band(UWB)based Multiple-Input Multiple-Output(MIMO)antenna with Defected Ground Structure(DGS)is employed.The proposed Electromagnetic Radiation Structures(ERS)is composed of multiple radiating elements.These MIMO antennas are designed and analyzed with and without DGS.The feeding is introduced by a microstrip-fed line to significantly moderate the radiating structure’s overall size,which is 60×40×1 mm.The high directivity and divergence characteristics are attained by introducing the microstripfed lines perpendicular to each other.And the projected MIMO antenna structures are compared with others by using parameters like Return Loss(RL),Voltage Standing Wave Ratio(VSWR),Radiation Pattern(RP),radiation efficiency,and directivity.The same MIMO set-up is redesigned with DGS,and the resultant parameters are compared.Finally,the Multiple Input and Multiple Output Radiating Structures with and without DGS are compared for result considerations like RL,VSWR,RP,radiation efficiency,and directivity.This projected antenna displays an omnidirectional RP with moderate gain,which is highly recommended for human healthcare applications.By introducing the defected ground structure in bottom layer the lower cut-off frequencies of 2.3,4.5 and 6.0 GHz are achieved with few biological effects on radio propagation in human body communications.The proposed design covers numerous well-known wireless standards,along with dual-function DGS slots,and it can be easily integrated into Wireless Body Area Networks(WBAN)in medical applications.This WBAN links the autonomous nodes that may be situated either in the clothes,on-body or beneath the skin of a person.This system typically advances the complete human body and the inter-connected nodes through a wireless communication channel.

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

Target estimation using MIMO radar with multiple subcarriers

This paper analyzes the effect of waveform parame- ters on the joint target location and velocity estimation by a non- coherent multiple input multiple output （MIMO） radar transmitting multiple subcarriers signals. How the number of subcarriers in- fluences the estimation accuracy is illustrated by considering the joint Cramer-Rao bound and the mean square error of the maxi- mum likelihood estimate. The non-coherent MIMO radar ambiguity function with multiple subcarriers is developed and investigated by changing the number of subcarriers, the pulse width and the frequency spacing between adjacent subcarriers. The numerical results show that more subcarriers mean more accurate estimates, higher localization resolution, and larger pulse width results in a worse performance of target location estimation, while the fre- quency spacing affects target location estimation little.

Shannon information capacity of time reversal wideband multiple-input multiple-output system based on correlated statistical channels

Utilizing channel reciprocity, time reversal（TR） technique increases the signal-to-noise ratio（SNR） at the receiver with very low transmitter complexity in complex multipath environment. Present research works about TR multiple-input multiple-output（MIMO） communication all focus on the system implementation and network building. The aim of this work is to analyze the influence of antenna coupling on the capacity of wideband TR MIMO system, which is a realistic question in designing a practical communication system. It turns out that antenna coupling stabilizes the capacity in a small variation range with statistical wideband channel response. Meanwhile, antenna coupling only causes a slight detriment to the channel capacity in a wideband TR MIMO system. Comparatively, uncorrelated stochastic channels without coupling exhibit a wider range of random capacity distribution which greatly depends on the statistical channel. The conclusions drawn from information difference entropy theory provide a guideline for designing better high-performance wideband TR MIMO communication systems.

A new layered space-time detection algorithm for frequency selective fading multiple-input multiple-output channels based on particle filter

In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.

Performance of terahertz channel with multiple input multiple output techniques

Terahertz(THz) communication is being considered as a potential solution to mitigate the demand for high bandwidth. The characteristic of THz band is relatively different from present wireless channel and imposes technical challenges in the design and development of communication systems. Due to the high path loss in THz band,wireless THz communication can be used for relatively short distances. Even,for a distance of few meters( > 5 m),the absorption coefficient is very high and hence the performance of the system is poor. The use of multiple antennas for wireless communication systems has gained overwhelming interest during the last two decades.Multiple Input Multiple Output( MIMO) Spatial diversity technique has been exploited in this paper to improve the performance in terahertz band. The results show that the Bit Error Rate( BER) is considerably improved for short distance( < 5 m) with MIMO. However,as the distance increases,the improvement in the error performance is not significant even with increase in the order of diversity. This is because,as distance increases,in some frequency bands the signal gets absorbed by water vapor and results in poor transmission. Adaptive modulation scheme is implemented to avoid these error prone frequencies. Adaptive modulation with receiver diversity is proposed in this work and has improved the BER performance of the channel for distance greater than 5 m.

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.

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.

Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

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。

基于时频域联合数据辅助MIMO均衡算法的多维度复用千公里级相干传输系统

基于少模光纤的模式复用系统利用单根纤芯中的正交模式实现各独立信道的并行传输,能够有效提升系统容量,解决单模光纤系统的容量危机.本文联合使用模式复用、波分复用与偏振复用技术,基于六模渐变型光纤实现了80个波长通道下32 Gbaud四模双偏振16阶正交振幅调制信号的1000 km相干传输,并在接收端使用时频域联合数据辅助8×8多输入多输出最小均方算法,以补偿由光纤传输导致的模式耦合及模间色散等线性损伤.与传统的纯时域或纯频域多输入输出最小均方均衡算法相比,该算法分别实现了57.1%的收敛速度提升与25.1%的收敛误差下降.经过1000 km少模光纤传输后,80通道每个模式及偏振态的误码率均低于开销为20%的软判决前向纠错编码阈值2.4×10^(-2),系统净速率达68.2 Tbit/s.

MIMO雷达分组正交波形集优化设计方法

在现代电子对抗中,数字射频存储(DRFM)设备能够快速截获机载脉冲多普勒雷达信号,能够实现对多输入多输出(MIMO)雷达的干扰。MIMO雷达可基于多组相互正交的波形集来对抗DRFM干扰。同时,为最大化MIMO雷达波形分集增益,每个脉冲内发射的波形也需要正交。为了平衡组内和组间的正交性,文中建立了一种分组正交波形集优化模型,其目标函数为组内和组间相关函数性能评估指标值的加权和;为了求解该优化问题,提出了一种分组正交波形集设计方法。所提方法将原优化问题简化为p-范数优化问题,基于MM算法导出了最小化目标函数的迭代求解表达式。仿真结果表明,所提方法可通过改变权重系数来灵活平衡MIMO雷达的干扰抑制性能和距离压缩性能。

基于SWIPT的无小区大规模MIMO-NOMA系统能量效率研究

无小区大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)与非正交多址接入(Non-Orthogonal Multiple Access,NOMA)都是未来6G的使能技术。无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)技术在进行信息解码的同时收集能量,与无小区大规模MIMO-NOMA优势互补。文中基于SWIPT研究无小区大规模MIMO-NOMA系统中的能量效率问题,通过联合优化功率分配系数和SWIPT的时隙切换(Time Switching,TS)系数,提高系统的能量效率。为了最大化能量效率,采用布谷鸟算法设计功率分配系数。考虑一种特殊情况,将所有终端的TS系数设置相同,进而推导了最佳TS系数的封闭表达式。仿真结果表明,相较于几种已有方案,文中提出的优化方案可以显著提升系统的能量效率。

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。

一种紧凑型低耦合超宽带MIMO天线设计

针对目前超宽带多输入多输出(ultra wideband multiple-input multiple-output,UWB-MIMO)天线存在尺寸大、隔离度低等不足,设计了一款紧凑型高隔离度UWB-MIMO天线。天线正面由“花瓶”形辐射贴片与阶梯形微带馈线组合而成,背面是具有切角的矩形接地板。通过对矩形辐射贴片对称切角、对接地板刻蚀矩形槽,实现天线的宽带化;通过在介质基板正反两面分别加载“鱼骨”形和改进对称“Y”形去耦枝节,实现天线的高隔离度。仿真与实测结果表明:天线尺寸较小,仅有22 mm×29 mm×0.8 mm,最终工作带宽可达1.5~18 GHz(相对带宽为169%),带内隔离度均高于17 dB,包络相关系数小于0.02。天线辐射性能良好且结构简单紧凑,可以很好地应用于UWB-MIMO无线通信系统中。

异质无蜂窝MIMO绿色通信网络

异质无蜂窝多天线(Multiple-Input Multiple-Output,MIMO)系统是未来移动网络发展的方向之一,也是极具节能潜力的高效通信系统。本文首先对异质无蜂窝MIMO系统的整体构架进行了介绍,从整体的角度分析该网络的具体构架和节能潜力,随后从绿色网络部署与协作、网络运行与调度以及用户接入、高能效MIMO收发机技术和传输与多址等方面介绍了基本原理和潜在节能增益。随着信息超材料的发展,一些全新的异质MIMO收发机逐渐展现了其在能效方面的优势,合理利用这些新型的收发机技术将有效提高系统能效。除了硬件带来的增益之外,异质无蜂窝组网的增益主要来源于各个接入点的协作传输所形成的超大规模MIMO,而这也带来了更加复杂的信号传输模式,同时受限于有限的算力资源,系统不可能无限协作,因此需要对该系统的接入点部署策略和协作方式进行全新的研究。在完成部署之后,由于用户在空间和时间上的通信需求变化较大,还可以利用深度学习等技术对用户的行为进行一定程度的预测,并在空间和时间上对通信设备的运行状态进行调整,在满足通信指标的同时提高系统的能效。最后,用户的多址接入与传输方式则是整个异质无蜂窝MIMO系统的基础,新型的速率分割多址接入(Rate-Splitting Multiple Access,RSMA)技术可以有效对抗干扰,而针对异质无蜂窝MIMO系统的传输波束赋形也需要进行全新的设计以适应RSMA技术和异质的MIMO设备。本文还同时讨论了以上各个方面的可能设计以及未来研究方向。

一种基于稀疏重构的多视角MIMO雷达关联成像算法研究

为改善复杂目标的成像表现、提升雷达图像的视觉效果,提出一种基于稀疏重构的多视角多输入多输出(multiple input multiple output,MIMO)雷达关联成像方法,设计了多视角雷达散射面积(radar cross section,RCS)起伏的稀疏重构关联成像模型。所提算法能提高辐射场信号随机性,改善复杂目标雷达关联成像参考信号与回波之间相关性退化的情况,减小多视角RCS起伏对相关性的影响,提升多视角下的关联成像质量。最后,通过仿真验证了所提方法的有效性。

宽带太赫兹大规模MIMO系统中的混合预编码

针对宽带太赫兹大规模多输入多输出(multiple input multiple output,MIMO)通信系统中更大的带宽和更多的天线导致的波束分裂问题,构建了基于时延的宽带太赫兹大规模MIMO系统的混合预编码通信模型,并提出了一种高效且可实现的联合时延和相位的宽带太赫兹混合预编码算法。考虑到时延器值的硬件限制,通过最小化最优模拟预编码与等效模拟预编码之间的差来联合优化模拟预编码矩阵和数字预编码矩阵,将联合优化问题转换成目标优化问题,通过将非凸问题转换成等价的凸问题来求全局最优解。仿真结果表明,提出的算法可以获得接近最优的可实现速率性能,且可以提高能量效率。

重叠阵元时分复用MIMO雷达多目标参数估计方法研究

针对多普勒-角度耦合和速度模糊问题,该文提出一种基于重叠阵元MIMO阵列的多目标参数估计方法。该方法基于虚拟孔径原理,在传统MIMO天线阵列中引入重叠阵元,构建重叠阵元MIMO天线阵列。通过在角度快速傅里叶变换(FFT)算法中引入循环迭代的方法估计阵列位置参数,利用重叠阵元回波信号的相位差值进行频率估计。同时,引入频谱搬移方法对速度区间进行转换,实现多目标的距离和速度估计。通过蒙特卡罗仿真实验,信噪比15 dB的条件下,解模糊正确率为100%,速度误差为0.1 m/s,角度误差为0.1°。基于城市交通场景采集的车辆数据集进行测试,测试结果表明,该方法能够实现对车辆目标的速度和角度精确估计,可满足交通雷达对车辆信息监测的实时性和准确性需求。

具有无线携能技术的MIMO-NOMA系统

用户干扰降低了毫米波大规模多输入多输出(MIMO)非正交多址接入(NOMA)系统的频谱效率和能量效率,针对这一问题,提出将无线携能技术(SWIPT)引入基于混合预编码(HP)的MIMO-NOMA系统,并设计了用户分组、混合预编码以及功率分配方案。首先,提出了簇头选择算法,以设计模拟预编码,基于等效信道相关性进行用户分组,并最大化等效信道增益设计数字预编码;其次,为追求用户间的公平性,提出一种基于二分的功率分配方案,以实现最小可达速率最大化。理论分析和仿真结果表明,信噪比(SNR)为5dB时,相较于现有的具有无限携能的正交多址方案,所提方案在全连接架构下频谱效率提升约24.8%,在子连接架构下能量效率提升约24.6%。