Anti-Jamming Null Space Projection Beamforming Based on Symbiotic Radio

With the development of information technology,more and more devices are connected to the Internet through wireless communication to complete data interconnection.Due to the broadcast characteristics ofwireless channels,wireless networks have suffered more and more malicious attacks.Physical layer security has received extensive attention from industry and academia.MIMO is considered to be one of the most important technologies related to physical layer security.Through beamforming technology,messages can be transmitted to legitimate users in an offset direction that is as orthogonal as possible to the interference channel to ensure the reception SINR by legitimate users.Combining the symbiotic radio(SR)technology,this paper considers a symbiotic radio antijamming MIMO system equipped with a multi-antenna system at the main base station.In order to avoid the interference signal and improve the SINR of the signal received by the user.The base station is equipped with a uniform rectangular antenna array,and using Null Space Projection(NSP)Beamforming,Intelligent Reflecting Surface(IRS)can assist in changing the beam’s angle.The simulation results show that NSP Beamforming could make a better use of the null space of interference,which can effectively improve the received SINR of users under directional interference,and improve the utilization efficiency of signal energy.

Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Specific Order and Bisection Search Aided Joint 3D Beamforming and RIS Reflecting Optimization

In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,the user node only has a single antenna due to a limited price.Maximization of its downlink spectral efficiency is a joint optimization problem of three variables,namely phase-shift matrixΦof RIS,tilt angleθand beamforming vector w used in BS 3D beamforming.We solve this problem by employing the alternating optimization(AO)algorithm.But,in each iteration,a specific optimization order of firstlyΦ,secondlyθand finally w is proposed,which facilitates the search of optimalθin the way of narrowing its trust region and enabling unimodal property over the narrowed trust region.It finally results in a better combination of{Φ,θ,w}.

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.

A deep learning driven hybrid beamforming method for millimeter wave MIMO system

The hybrid beamforming is a promising technology for the millimeter wave MIMO system,which provides high spectrum efficiency,high data rate transmission,and a good balance between transmission performance and hardware complexity.The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams.However,the Neural network Hybrid Beamforming(NHB)adopts a totally different strategy,which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy.Driven by the Deep Learning(DL)hybrid beamforming,in this work,we propose a DL-driven nonorthogonal hybrid beamforming for the single-user multiple streams scenario.We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate(BER)performance than the orthogonal hybrid beamforming even with the optimal power allocation.Inspired by the NHB,we propose a new DL-driven beamforming scheme to simulate the NHB behavior,which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming.Moreover,our simulation results demonstrate that the DL-driven nonorthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of subconnected schemes and imperfect Channel State Information(CSI).

Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer

Reconfigurable intelligent surface(RIS)employs passive beamforming to control the wireless propagation channel,which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer(WPT)systems.Such beamforming schemes are classified as discrete and non-convex integer program-ming problems.In this paper,we propose a Monte-Carlo(MC)based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic(EM)WPT systems.Generally,the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples.And the sample with the maximum received power is considered the optimal solution.In order to adapt to the application scenarios,we develop two types of passive beamforming algorithms based on such MC sampling methods.The first passive beamforming uses an approximation of the integer programming as the initial sample,which is calculated based on the channel information.And the second one is a purely randomized algorithm with the only total received power feedback.The proposed methods present several advantages for RIS control,e.g.,fast convergence,easy implementation,robustness to the channel noise,and limited feedback requirement,and they are applicable even if the channel information is unknown.According to the simulation results,our proposed methods outperform other approxi-mation and genetic algorithms.With our methods,the WPT system even significantly improves the power effi-ciency in the nonline-of-sight(NLOS)environment.

On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power transmitter.However,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power transmitter.Specifically,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in practice.In conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

Fractional Processing Based Adaptive Beamforming Algorithm

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing complexity.The goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity lower.The effectiveness of the algorithm will be studied and evaluated in this context.In this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of resources.This algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational complexity.The FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight adaptation.The derivation of the algorithm is provided and supported by mathematical convergence analysis.Performance is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various parameters.The results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter plots.FLMS outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

Robust beamforming for IRS-aided SWIPT in cognitive radio networks

Intelligent reflecting surface(IRS)is widely recognized as a promising technique to enhance the system perfor-mance,and thus is a hot research topic in future wireless communications.In this context,this paper proposes a robust BF scheme to improve the spectrum and energy harvesting efficiencies for the IRS-aided simultaneous wireless information and power transfer(SWIPT)in a cognitive radio network(CRN).Here,the base station(BS)utilizes spectrum assigned to the primary users(PUs)to simultaneously serve multiple energy receivers(ERs)and information receivers(IRs)through IRS-aided multicast technology.In particular,by assuming that only the imperfect channel state information(CSI)is available,we first formulate a constrained problem to maximize the minimal achievable rate of IRs,while satisfying the harvesting energy threshold of ERs,the quality-of-service requirement of IRs,the interference threshold of PUs and transmit power budget of BS.To address the non-convex problem,we then adopt triangle inequality to deal with the channel uncertainty,and propose a low-complexity algorithm combining alternating direction method of multipliers(ADMM)with alternating optimi-zation(AO)to jointly optimize the active and passive beamformers for the BS and IRS,respectively.Finally,our simulation results confirm the effectiveness of the proposed BF scheme and also provide useful insights into the importance of introducing IRS into the CRN with SWIPT.

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.

一种改善beamforming“延迟求和”算法精度的方法

分析了传统"延迟求和"波束形成(beamforming)相控空间扫描原理,并进行了仿真实验研究,仿真表明由于旁瓣、栅瓣等问题的存在使信号源方位识别不准确。针对此问题本文提出了一种基于beamforming方向图矩阵进行声源定向的方法,理论分析和实验表明该方法能够有效地提高分辨率,提高定位的准确性,并基于该原理提出了用于工程的算式。

时延反馈下的自适应STBC-Beamforming跨层设计

为提高无线通信网络的频谱效率,提出一种自适应空时分组编码波束成形(STBC-beamforming)跨层设计方案.该方案利用自适应编码调制技术与混合自动重传技术交互协作,使发射端可以根据反馈延时信息自适应调节调制模式,选择发射权以及自动重传数据.推导了系统在时延反馈条件下的频谱效率和中断概率的闭合表达式,分析了时延反馈对系统性能的影响.仿真结果表明,对比单发/单收跨层系统和Alamouti’s跨层系统,自适应STBC-beamforming跨层系统的性能有明显提高.

闭环多用户多天线Beamforming系统的性能分析

0引言无线通信系统需要能提高频谱效率的更加智能的技术,以满足无线频谱资源的日益匮乏。波束成型Beamforming和多输入多输出技术(MIMO,Multi Input Multi Output)都可以在基站使用多根天线,利用多天线阵列提供波束增益和发射分集,从而提高系统的性能[1,2]。随着发射天线的增加,可以改善系统的性能;但只有当系统有两根发射天线时,正交空时分组编码才能获得全速率和全发射分集增益。

MmWave Beamforming for UAV Communications with Unstable Beam Pointing

Millimeter wave(mmWave) communications of unmanned aerial vehicles(UAVs) have drawn dramatic attentions for its flexibility on a variety of applications.Recently,channel tracking base on the spatial features has been proposed to solve the problem of beam misalignments due to the UAV navigation.However,unstable beam pointing caused by the non-ideal beam tracking environment may impact the performance of mmWave systems significantly.In this paper,an improved beamforming method is presented to overcome this shortcoming.Firstly,the effect of the beam deviation is analyzed through the establishment of the equivalent data rate.Then,combining the quantification of spatial angle and the improved orthogonal matching pursuit(OMP) algorithm,an optimized beam corresponding to the beam deviation is obtained.Simulation results show that the optimized beam of the proposed approach can effectively improve the spectral efficiency without improving the complexity when the beam pointing is unstable.

Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

Distributed Transmit Beamforming for UAV to Base Communications

Distributed transmit beamforming(DTB) is very efficient for extending the communication distance between a swarm of UAVs and the base,particularly when considering the constraints in weight and battery life for payloads on UAVs.In this paper,we review major function modules and potential solutions in realizing DTB in UAV systems,such as timing and carrier synchronization,phase drift tracking and compensation,and beamforming vector generation and updating.We then focus on beamforming vector generation and updating,and introduce a concatenated training scheme,together with a recursive channel estimation and updating algorithm.We also propose three approaches for tracking the variation of channels and updating the vectors.The effectiveness of these approaches is validated by simulation results.

Order selection in fractional Fourier transform based beamforming

Traditionally,beamforming using fractional Fourier transform（FrFT） involves a trial-and-error based FrFT order selection which is impractical.A new numerical order selection scheme is presented based on fractional power spectra（FrFT moment） of the linear chirp signal.This method can adaptively determine the optimum FrFT order by maximizing the second-order central FrFT moment.This makes the desired chirp signal substantially concentrated whereas the noise is rejected considerably.This improves the mean square error minimization beamformer by reducing effectively the signal-noise cross terms due to the finite data length de-correlation operation.Simulation results show that the new method works well under a wide range of signal to noise ratio and signal to interference ratio.

Minimum geometric power distortionless response beamforming against heavy-tailed noise of unknown statistics

A minimum geometric power distortionless response beamforming approach against impulsive noise （including all α- stable noise） of unknown statistics is proposed. Due to that definite logarithmic moments require no priori knowledge of impulsive noise, this new beamformer substitutes the logarithmic moments for the second-order moments and iteratively minimizes the ＂ge- ometric power＂ of the beamformer.s output snapshots, subjected to a linear constraint. Therefore, the proposed beamformer can provide significantly higher output geometric signal-to-noise-andinterference ratio. Moreover, the optimum weight vector is obtained by using a new iteration process. The simulation results prove that the new method is effective.

Implementation of broadband low-sidelobe beamforming in time domain

In modern active and passive sonar systems, broadband beamforming for acoustic arrays is widely used to suppress unwanted interference and to detect target signals of interest. A broadband low sidelobe beamforming scheme in time domain is proposed in this paper. The first step of this scheme is to delay the outputs of each element in the acoustic array by a tapped-delay-line (TDL) to accomplish the integer part of the time delay need to form a beam. Then, finite impulse response (FIR) digital filters are used to implement the fractional part of the time delay. The weighting coefficients for all array elements at different frequencies to realize the low sidelobe beams are also implemented with the FIR digital filters. Finally, the outputs of the digital filters are summed up to yield the time domain beam output. The design of low sidelobe beam pattern and that of the FIR digital filters are two crucial technical issues in this beamforming procedure. The low sidelobe beams of each sub-band are designed using the optimized beam synthesis approach based on the principle of MVDR beamforming. An improved adaptive approach are used for the design of FIR digital filters, and the design requirements of these filters were specified by the weights of low sidelobe beams of each sub-band over the broad frequency band. Results of computer simulation for a twelve-element arc array show that the beamforming scheme is very effective in forming low sidelobe broadband beam.

Robust Beamforming for Secured Wireless Power Transfer in MIMO Magnetic Resonant Coupling System:A Probabilistic Approach

Wireless power transfer(WPT) to support mobile and portable devices is an emerging wireless technique.Among all kinds of approaches,magnetic resonance coupling(MRC) is an excellent one for mid-range WPT,which provides better mobility,flexibility,and convenience due to its simplicity in hardware implementation and longer transmission distances.In this paper,we consider an MRCWPT system with multiple power transmitters,one intended power receiver and multiple unintended power receivers.We investigate the probabilistic robust beamforming designs and provide efficient algorithms to achieve the local optimums under two different criteria,i.e.,total source power minimization problem and min-max unintended receiving power restriction problem.As the problems are quite typical in robust design situations,our proposed robust beamformers can be conveniently applied to other probabilistic robust design problems,thus reduce the complexity as well as improve the beamforming performance.Numerical results demonstrate that the proposed algorithms can significantly improve the performance as well as the robustness of the WPT system.