Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.

Deconvolved Beamforming Using the Chebyshev Weighting Method

This paper studies a deconvolved Chebyshev beamforming(Dcv-Che-BF)method.Compared with other deconvolution beamforming methods,Dcv-Che-BF can preset sidelobe levels according to the actual situation,which can achieve higher resolution performance.However,the performance of Dcv-Che-BF was not necessarily better with a lower preset sidelobe level in the presence of noise.Instead,it was much better when the preset side lobe level matched the signal to noise ratio of the signal.The performance of the Dcv-Che-BF method with different preset sidelobe levels was analyzed using simulation.The Dcv-Che-BF method achieved a lower sidelobe level and better resolution capability when the preset sidelobe level was slightly greater than the noise background level.To validate the feasibility and performance of the proposed method,computer simulations and sea trials were analyzed.The results show that the Dcv-Che-BF method is a robust high-resolution beamforming method that can achieve a narrow mainlobe and low sidelobe.

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

Minimum variance adaptive beamforming combined with coherence factor weighting applied to ultrafast active cavitation imaging

The ultrafast active cavitation imaging(UACI)based on plane wave transmission and delay-and-sum(DAS)beamforming has been developed to monitor cavitation events with a high frame rate.However,DAS beamforming leads to images with limited resolution and contrast.In this paper,minimum variance(M V)adaptive beamforming and coherence factor(CF)weighting are combined to achieve an MVCF-based UACI,which can improve the cavitation imaging quality.The detailed algorithm evaluation has been investigated from both simulation and experimental data The simulation data include10point targets and a cyst,while the experimental data are obtained by detecting the dissipation of cavitation bubbles in water excited by a single element transducer with frequency of1.2MHz.The advantages of the proposed methodology as well as the comparison with conventional B-mode,DAS?M V,DAS-CF and MV on the basis of compressive sensing(CS)(called MVCS)beamformers are discussed.The results show that MVCF beamformer has a significant improvement in terms of both resolutions and signal-to-noise ratio(SN R).The MVCF-based UACI has a SNR at21.82dB higher,lateral and axial resolution at2.69times and1.93times?respectively,which were compared with those of B-mode active cavitation mapping.The MVCF-based UACI can be used to image the residual cavitation bubbles with a higher SNR and better spatial resolution

Multi-objective robust secure beamforming for cognitive satellite and UAV networks

A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with the UAV network,we first consider both achievable secrecy rate maximization and total transmit power minimization,and formulate a multi-objective optimization problem(MOOP)using the weighted Tchebycheff approach.Then,by supposing that only imperfect channel state information based on the angular information is available,we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones.Next,we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector.Finally,simulation results illustrate that the Pareto optimal trade-off can be achieved,and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

Digital Beamforming by Multiple Chirp Zeta Transforms for 3-D Sonar Imaging Systems

The computational load is prohibitive for real-time image generation in 3-D sonar systems, particularly when the steering angle approximation is required. In this paper, a novel multiple Chirp Zeta Transforms （MCZT） beamforming method in frequency domain is being proposed. The single long-length Chirp Zeta Transform （CZT） in the original CZT beamforming is replaced by several CZTs with smaller lengths for different partitions along each dimension. The implementing routine of the algorithm is also optimized. Furthermore, an avenue to evaluate the estimating error for the angle approximation in 3-D imaging applications is presented, and an approach to attain valid partitions for the steering angles is also flhistrated. This paper demonstrates a few advantages of the proposed frequency-domain beamforming method over existing methods in terms of the computatianal complexity.

可重构智能表面辅助双功能雷达通信系统的联合波束优化

双功能雷达通信系统(DFRC)是有效解决未来网络频谱资源拥挤问题的理想技术之一,该文引入了可重构智能表面(RIS)技术,旨在提升用户的加权和速率和系统的探测性能。首先在雷达功率约束和可重构智能表面的恒定模约束以及通信的整体功率预算下,构建了通信用户的加权和速率和系统探测性能最大化的优化模型。通过联合优化基站的主动波束和可重构智能表面的无源被动波束形成,该文设计了一种有效的基于加权最小均方误差、分式规划和流形优化的交替优化算法,将非凸优化问题转化为两个子问题并利用迭代进行求解。仿真结果表明,所提方案对解决该问题的有效性和对用户加权和速率在较低迭代次数下达到收敛,并且可使用户的加权和速率上限提升0.86 bit/(s·Hz)和使系统探测更具方向性。

基于测深侧扫声呐的DOA估计算法研究

针对测深侧扫声呐进行波达方向(Direction of Arrival,DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续线阵划分为多个子阵,并将各个子阵在预估计方向做加权波束形成;再次,采用加权子空间拟合(Weighted Subspace Fitting,WSF)算法构造代价函数;最后,采用阻尼牛顿法求解得到高精度的DOA估计结果。仿真结果表明,文中所提算法在阵元出现幅度相位误差条件下的角度估计均方误差相对于WSF算法减少了约0.03°。海试数据分析结果表明,文中所提算法的测深点均方误差整体优于WSF算法,其相对测深精度提高了约9.8个百分点。以上分析结果表明,文中所提算法整体优于WSF算法,可以实现在阵元幅度相位误差及低信噪比情况下的高精度DOA估计。

基于SDW-MMSE的广义特征值稳健波束形成方法

最大输出信噪比(Signal-to-noise ratio,SNR)准则下,广义特征值(Generalized eigenvalue,GEV)波束形成存在复系数难以控制的问题,在复杂的声学环境中容易导致输出信号严重失真。针对复系数估计问题,本文提出一种基于最小均方误差(Minimum mean square error,MMSE)的复系数估计方法,并通过引入语音失真权重因子(Speech distortion weight,SDW),调节降噪效果和语音失真之间的权重关系,进而提出了基于SDW-MMSE的广义特征值稳健波束形成方法。通过最大似然法估计目标信号和噪音信号的功率谱,进而求解主广义特征向量。进一步基于SDW-MMSE估计复系数,将复系数与主广义特征向量相结合,从而得到基于SDW-MMSE的广义特征值稳健波束形成滤波向量。仿真实验结果表明,本文提出的波束形成方法可有效消除相干噪声和非相干噪声,具有输出信噪比高、语音失真少等稳健性能。

STAR-RIS辅助ISAC系统波束赋形优化方法

本文提出了将通信感知一体化(Integrated Sensing and Communication,ISAC)系统与同时透射和反射可重构智能表面(Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces,STAR-RIS)相结合的系统模型,解决了传统可重构智能表面RIS不能实现全空间通信与感知的问题;同时考虑在STAR-RIS上安装低成本的专用传感器以实现ISAC系统在STAR-RIS上执行目标感知的一种新颖有源架构,解决了雷达感知的严重路径损耗问题.本文旨在联合优化ISAC基站处的波束赋形和STAR-RIS的无源波束赋形,以最大化通信用户的加权和速率(Weighted Sum Rate,WSR),同时保证感知性能的最低信噪比(signal-to-noise ratio,SNR).为了解决该复杂非凸优化问题,交替优化基站波束赋形及STAR-RIS无源波束赋形.针对所提的满足雷达感知SNR最低要求下最大化WSR问题,基站处波束赋形的优化子问题等价为加权最小均方误差(Weighted Minimum Mean Square Error,WMMSE)问题,STAR-RIS处无源波束赋形优化子问题等价为分式规划(Fractional Programming,FP)问题.进一步,分别将优化的非凸子问题转化为二次约束二次规划(Quadratic Constraint Quadratic Programming,QCQP),并使用半正定松弛(Semidefinite Relaxation,SDR)技术将它们分别转化为凸的半正定规划(Semidefinite Programming,SDP)子问题进行迭代求解.仿真结果验证了所采用新型STAR-RIS辅助ISAC方案的优点和所提算法在提高WSR性能上的有效性.

A parallel complex divider architecture based on DCD iterations for computing complex division in MVDR beamformer

This paper presents a hardware architecture using mixed pipeline and parallel processing for complex division based on dichotomous coordinate descent(DCD) iterations. The objective of the proposed work is to achieve low-latency and resource optimized complex divider architecture in adaptive weight computation stage of minimum variance distortionless response(MVDR)algorithm. In this work, computation of complex division is modeled as a 2×2 linear equation solution problem and the DCD algorithm allows linear systems of equations to be solved with high degree of computational efficiency. The operations in the existing DCD algorithm are suitably parallel pipelined and the performance is optimized to 2 clock cycles per iteration. To improve the degree of parallelism, a parallel column vector read architecture is devised.The proposed work is implemented on the field programmable gate array(FPGA) platform and the results are compared with state-of-art literature. It concludes that the proposed architecture is suitable for complex division in adaptive weight computation stage of MVDR beamformer. We demonstrate the performance of the proposed architecture for MVDR beamformer employed in medical ultrasound imaging applications.

频域加权陷波多声发射源定位与伪源辨识方法

针对信号处理中的稀疏阵列有时会出现栅瓣和伪峰的现象,进而影响波束形成法在结构损伤定位中应用的问题,提出了基于频域加权波束零陷的未知真实声发射源数量的声发射定位与伪源辨识方法。首先,推导研究了波束零陷思想在二维频域加权波束形成方法中的作用机理;然后,基于频域加权陷波的波束形成方法,提出了在未知真实声发射源数量情况下的伪声发射源的辨识和真实声发射源的定位流程;最后,在存在较多伪声发射源干扰情况下进行了单声发射源和同时多声发射源的定位试验,验证了所提出方法的准确性。研究结果表明,所提出的方法对于推广波束形成法在结构健康监测领域的应用具有价值。

Log Anomaly Detection Through GPT-2 for Large Scale Systems

As the scale of software systems expands,maintaining their stable operation has become an extraordinary challenge.System logs are semi-structured text generated by the recording function in the source code and have important research significance in software service anomaly detection.Existing log anomaly detection methods mainly focus on the statistical characteristics of logs,making it difficult to distinguish the semantic differences between normal and abnormal logs,and performing poorly on real-world industrial log data.In this paper,we propose an unsupervised framework for log anomaly detection based on generative pre-training-2(GPT-2).We apply our approach to two industrial systems.The experimental results on two datasets show that our approach outperforms state-of-the-art approaches for log anomaly detection.

L型阵列内插区域对称扩展波束形成

针对内插变换中角度敏感的问题,提出一种内插区域对称扩展优化的波束形成算法。将L型阵列的阵列流形矩阵关于坐标轴做对称处理,实现俯仰角与方位角内插变换区域的二倍对称扩展;通过现有可变对角加载技术的改进及Taylor加权进行波束形成权矢量优化。仿真结果表明,该算法有效地扩大了系统可用的内插变换角度范围,并有效约束L型扩展阵列的波束形成的旁瓣电平,同时提高其输出信干噪比。

一种加权稀疏约束稳健Capon波束形成方法

为了克服标准Capon波束形成器旁瓣级高以及存在角度失配时性能急剧下降等缺点,在稀疏约束Capon波束形成器的基础上,提出了一种加权稀疏约束Capon波束形成器.该方法利用波束响应的稀疏分布特性,在标准Capon波束形成优化模型中加入旁瓣区域波束响应稀疏约束(?1范数约束),使旁瓣区域波束响应向量中非零元素的个数最小化;通过阵列采样数据协方差矩阵特征分解得到信号子空间及噪声子空间,利用信号子空间与噪声子空间的正交特性,构造加权矩阵对稀疏约束进行加权,使得稀疏重构时波束响应向量中不同角度对应的元素得到不同程度的约束.该方法有效地抑制了Capon波束形成器的高旁瓣级,加深了干扰方位零陷,提高了阵列输出信干噪比.由于稀疏约束,波束响应向主瓣集中,期望信号方向附近的波束响应都较大,从而也提高了阵列抗导向矢量角度失配的能力.数值仿真和水池实验验证了所提方法的有效性.

一种分解迭代二阶锥规划鲁棒自适应波束形成算法

为有效克服导向矢量大失配误差对自适应波束形成器的影响,该文提出了一种最差性能最优的分解迭代鲁棒自适应波束形成算法。该算法对非凸的幅度响应约束问题进行分解处理,将问题转化为迭代的二阶锥规划问题,从而可对鲁棒响应区的波束宽度和纹波水平进行自由控制,并可得到较高的输出信干噪比。此外,与现有大部分该类鲁棒波束形成方法相比,提出的算法直接对权矢量进行优化,无需使用谱分解算法,避免了阵列结构的限制,可适用于任意阵形。仿真结果验证了算法的正确性和有效性。

超宽带穿墙雷达成像技术研究

文中详细介绍了超宽带穿墙雷达试验系统及其数据处理技术。针对试验系统中基于延时-求和波束形成方法的传统超宽带成像算法目标分辨率低、容易产生误判的缺点,引入最小方差无畸变响应波束加权及相关因子加权,优化成像算法,并应用于墙后隐藏目标的检测、成像试验。外场试验表明,应用优化后的成像算法,系统能对未知场景中的目标给予准确定位,目标杂波比改善了9.41 dB。

相干环境下LCMV自适应阵列抗干扰问题研究

相干干扰环境下,自适应阵列性能会急剧下降,常用的处理方法是空间平滑。然而,采用常规均匀空间平滑的自适应阵列对相干干扰的抑制能力较差,而且会损失阵列孔径。该文提出一种改进的相干干扰抑制方案:首先,提出了一种自适应的加权空间平滑(WeightedSpatialSmoothing,WSS)算法,它将各子阵的相关矩阵进行加权平均,可以最大程度地解相干;然后,在WSS基础上,利用线性约束最小方差(LCMV)准则得到子阵波束形成器最佳权矢量;最后,提出了一种利用子阵间的相位关系对全阵做波束形成的方法。该方案大大提高了阵列对抗相干干扰的能力,同时避免了一般空间平滑后阵列的孔径损失。理论分析和仿真结果说明了方法的有效性和稳健性。

相干多径环境下自适应阵列的性能改进

针对传统自适应波束形成算法无法抑制相干干扰的问题,引入了一种预处理技术——自适应加权空间平滑(Weighted Spatial Smoothing,WSS)算法,结合线性约束最小方差准则,可以有效地抑制相干干扰。同时,针对WSS引起的孔径损失,提出了一种对子阵进行相位补偿,利用全阵做波束形成的方法。理论分析和仿真结果表明,该方法对相干干扰的抑制能力明显优于常规均匀空间平滑(Uniform Spatial Smoothing,USS)算法,而且避免了孔径损失。

加权直接数据域波束形成

为解决直接数据域(direct data domain,DDD)算法波束形成旁瓣电平高的问题,在约束优化的基础上提出了加权DDD波束形成算法。加权算法首先根据波束指向或者预成波束方向给出合适的主瓣宽度,然后在旁瓣区域约束最高旁瓣电平的高度,达到旁瓣抑制的效果。仿真分析了固定旁瓣电平变化主瓣宽度和固定主瓣宽度变化旁瓣电平两种约束优化形式。结果表明,加权DDD波束形成具有良好性能,能在预设主瓣宽度略宽于原波束主瓣宽度时,旁瓣电平能够满足预设要求。